A Modular, Cyclic Neural Network for Character Recognition
|
|
- Deirdre McDonald
- 6 years ago
- Views:
Transcription
1 A Modular, Cyclic Neural Network for Character Recognition M. Costa, E. Filippi and E. Pasero Dept. of Electronics, Politecnico di Torino C.so Duca degli Abruzzi, TORINO - ITALY Abstract We present a multi-layer feed-forward neural network that has been built up for pattern recognition purposes. Since we put special emphasis onto the aptitude towards hardware implementation, we provided it with a modular architecture of partially connected subnets. At its turn, this special organization makes it possible to avoid random initialization of the weights. The learning strategy is characterized by the adaptation of the learning rate coupled with an intermediate "batching" of the error back-propagation. To test the performance of the net in the classification of hand-written numerals, we fed the input units with grey-shaded patterns obtained from a large data set of the US National Institute of Standards and Technology (NIST). The original samples have been subjected only to scaling operations. The results up to now obtained seem quite interesting. In fact, in this application our recognizer ranks well among the ten best-rated OCR systems which competed in a world-wide contest organized by NIST itself in June Introduction Multi-Layer Perceptrons (MLPs) are presently used in a large variety of pattern recognition tasks, so they really constitute a clear example of general-purpose devices. However, when dealing with real-world applications like OCR, MLPs are seldom utilized in a plain form: in fact they are usually tailored to specific requirements, and sometimes they become part of more complex classifying systems. This stems from the fact that, although its operation principle is quite straightforward, yet the design of an efficient MLP classifier constitutes a complex task, since at least three main issues are to be considered: 1) Experimental results clearly show that performance cannot be set free from the way information is encoded into the examples. Data Preprocessing strategy thus assumes major relevance, in that it can dramatically affect the final result as well as the amount of resources that suffices to achieve it; 2) Given the application, we would like to determine the optimal Network Architecture by means of few specifications and design rules. As a matter of fact, such process instead relies upon heuristic choices; 3) The Learning Procedure usually involves several parameters. Again, their values must be determined mainly on a trial-and-error basis, especially if advanced techniques like weight decay [1] are adopted. It is thence evident that additional hints are needed in order to properly address the design strategy. In fact, several solutions have been proposed that exploit suggestions often provided by the application itself. For instance, several works are concerned with prior extraction of relevant features from the raw data: this is done by means of traditional algorithms [2], by setting up hybrid
2 networks [3,4], or via highly constrained architectures with several hidden layers [5] that apply to MLPs some ideas owned by the Neocognitron [6]. An alternative interesting approach leads to the definition of MLP committees in conjunction with data resampling and the generation of synthetic patterns [7]. On the other side, we must take into account the existence of conventional techniques that already proved to be very effective, especially in the OCR field [8]. Compared to them, Artificial Neural Networks can rely on their own intrinsic, massive parallelism; but this winning characteristic cannot be exploited by software simulations on serial processors. So we think that it is required to give priority importance to the hardware feasibility of the proposed solutions. This concept, while forcing us to cope with stringent constraints, amazingly turned into a guideline along the formulation of those answers to the above issues that are described in the following sections. 2 Data Preprocessing For our training experiments we chose a database of hand-written digits collected by the US National Institute of Standards and Technology (NIST). It consists of samples stored as binary images inside matrices of 128*128 pixels each. We performed only scaling operations on the original data to produce patterns with specified dimensions and number of grey levels. For this purpose we developed two different algorithms: the first one forces the character to "touch" all four borders of the output image, while the other one preserves its original aspect ratio. At first we utilized such procedures to build two distinct training sets of 16*16 binary patterns. We then carried out several simulations using two copies of the same MLP to assess the best alternative solution. Unfortunately, in any case we did not obtain encouraging results. However, we achieved substantial improvements by averaging the responses of the two nets. This fact suggested the opportunity of feeding a single MLP with both versions of the same data. In order to reduce the number of input components without losing information, we decided to use smaller patterns (8*8) with an higher number of grey levels (64). Fig. 1 shows some examples of patterns that have been subjected to this kind of twofold preprocessing (only 5 grey levels are displayed). Fig. 1
3 3 Network Architecture In view of future hardware implementation, we identified three primary requirements to be followed in the architectural definition of the net: 1) Every neuron must have a limited number of synapses (we imposed a maximum of 32 plus the threshold); 2) Interconnections must be planned so as to allow an easy routing of the communication lines; 3) No additional constraints specifically related to OCR are to be imposed, so in principle the same solution can be directly utilized for other classification tasks, or scaled to fit their requirements. We therefore designed a MLP provided with modular architecture. The number of modules equals that of the classes to be distinguished. Every module can be viewed as a partially connected subnet with only one output neuron. Fig. 2 emphasizes the general organization of the net: in particular, it can be seen that different modules do not share any connection. We can take great advantage of this, because we can plan to physically realize only few modules and then multiplex them. Their actual number can be settled to allow an efficient pipelining of the preprocessing stage with proper MLP operation. Moreover, this highly parallelizable structure guarantees low spatial cross-talk among hidden neurons, thus resulting in a fairly high convergence rate during the training phase [9]. Fig. 3 shows the inner structure of the single module. The output neuron is completely connected with the hidden layer of 32 elements. At its turn, each hidden unit has access to only 28 input components in a cyclic, sequential fashion: i.e., inputs 1-28 are connected to the first neuron, inputs are connected to the second neuron,.., inputs and 1-12 are connected to the fifth neuron (that depicted dark in the figure), and so on. These choices take care of two important features: first, each module covers the input vector an integer number of times, so that the very same connection scheme is preserved along the network; second, different hidden neurons in the same module are connected with different subsets of the input vector. General Architecture of the Modular MLP Outputs Connection Scheme of Each Module Output Neuron Modular Connections Synapses Hidden 32 Hidden Hidden... Hidden Layer of 32 Units with 28 Synapses Each... Partial Connections 128 Inputs 128 Inputs with Cyclic, Sequential Access Fig. 2 Fig. 3
4 4 Learning Procedure The special architecture of the net had a profound impact on the learning strategy itself. In fact, we noted that in this case random initialization of the weights could be avoided. We therefore started with null values (an entire class of MLPs with logistic neurons and generic number of hidden layers can be initialized in this way. Details are in [10] ). Such a chance carries some interesting properties: 1) We get rid of one heuristic parameter, i.e. the maximum absolute value of the initial random weights; 2) Since neurons are provided with logistic transfer function, they lie in the farthest state from saturation. In other words, they are maximally sensitive to the error signal. Of course, it was necessary to avoid the sudden spreading of weight values towards a substantially random distribution after few updatings. To do this, two solutions appeared that took the serious drawback of slowing down of the system evolution. That is: 1) Performing a by epoch training; 2) Using low learning rate. Concerning the first point, we found a satisfactory trade-off by making one update every 100 patterns presented to the net, thus realizing an intermediate "batching" of the error backpropagation. We then started with low learning rate (0.01), and then changed its value according to the Vögl adaptive technique [11]. After 100 training epochs, we halved all the weights and kept on with the same procedure for 50 additional epochs. Although this operation can be considered a very crude form of weight decay, nevertheless it have already proven to be quite effective [12]. Figures 4 and 5 show the behaviours of the learning rate and of the Mean Square Error (MSE) on the outputs vs. the number of training epochs. It should be noted that, as long as the learning rate increases, MSE tends to saturate until it stops decreasing. When this happens, the learning rate gets halved, thus allowing narrower zones of the error surface to be explored. As a result, MSE starts going down again. Fig. 4 Fig. 5
5 5 Performance Evaluation In June 1992, NIST organized a world-wide contest with the purpose of evaluating the state of the art in the OCR field. For what concerns hand-written numerals, NIST provided a suggested training set (the one we previously described) and a test set of samples purposely taken from a very different population. Therefore, recognition performance on the latter database is very revealing about the generalization capability of the system. We preprocessed such samples in the same way we did for their training counterparts (except for the fact that in this case we used only 16 grey levels), and then we tried to classify them by means of our modular MLP. In particular, we were mainly interested in checking the behaviour of the net when constraints on the resolution of both memory and computing elements are applied [13,14]. We then quantized all the weights using 6 bits, and the transfer function of the hidden neurons using 4 bits. Here output neurons are not involved. In fact, since their transfer function is monotonic, we can determine the winning class directly on their activations. In analog implementations these quantities are usually expressed in terms of currents, and very simple circuits can be designed to select the highest one [15]. With this configuration, when we forced the net to take a decision anyway, we achieved 3.69% error rate on the NIST Test Set. It is worth nothing that performance worsening is very limited in comparison with the usage of floating-point weights and neurons, since it amounts to about 0.1%. This stems from the fact that weight values result more uniformly distributed once decay is applied and additional training epochs performed. We then rejected the most dubious cases by imposing lower thresholds onto the winning outcome: clearly this is not the most effective solution, since the amount of information provided by the net is not fully taken into account. However, it has been chosen for its simplicity. Fig. 6 summarizes the results we obtained: dots in the graph show the behaviour of the error rate with regard to the percentage of rejected samples. Fig. 6 6 Conclusions We showed how a large, real-word task like the recognition of hand-written numerals may be efficiently and economically accomplished by means of a rather general-purpose MLP. In fact, our classifier correctly recognized more than 96.3% of the samples contained in the NIST Test Set.
6 Therefore, it ranks sixth in the corresponding graduated list even in the presence of the constraints we imposed for hardware feasibility purposes. We want to point out here the plainness of the solutions that allowed us to achieve this result: no complex features are extracted from the raw data, no "a priori" knowledge about the problem to be solved is used in the architectural definition of the net. Moreover, the system has a total amount of 9610 free parameters: so it is about as large as a completely connected MLP with 128 inputs, one hidden layer of only 69 units, 10 outputs. Acknowledgements This work is partly supported by Italian "Programma Nazionale di Ricerca sulle Tecnologie per la Bioelettronica" References [1] Krogh A. and Hertz J. A., "A Simple Weight Decay can Improve Generalization", in Advances in Neural Information Processing Systems 4, Moody J. E., Hanson S. J. and Lippmann R. P. (eds.), Morgan Kauffman Publishers, San Mateo, California, 1992 [2] Garris M. D., Wilkinson R. A. and Wilson C. L., "Methods for Enhancing Neural Network Handwritten Character Recognition", in Proc. of the International Joint Conference on Neural Networks, Vol. 1, San Diego, California, 1991 [3] Costa M. and Pasero E., "FNC: a Fuzzy Neural Classifier with Bayesan Engine", in Proc. of Italian Workshop on Neural Nets, Vietri sul Mare (SA), Italy, 1993 [4] Iwata A., Tohma T., Matsuo H. and Suzumura N., "A Large Scale Neural Network "CombNET" and its Application to Chinese Character Recognition", in Proc. of the International Neural Network Conference, Vol. 1, Paris, France, 1990 [5] LeCun Y., Boser B. E., Denker J. S., Henderson D., Howard R. E., Hubbard W. and Jackel L. D., "Handwritten Digit Recognition with a Back-Propagation Network", in Advances in Neural Information Processing Systems 2, Touretzky D. S. (ed.), Morgan Kauffman Publishers, San Mateo, California, 1990 [6] Fukushima K. and Wake N., "Handwritten Alphanumeric Character Recognition by the Neocognitron", in IEEE Transactions on Neural Networks, Vol. 2, No. 3, May 1991 [7] Drucker H., Schapire R. and Simard P., "Improving Performance in Neural Networks Using a Boosting Algorithm", in Advances in Neural Information Processing Systems 5, Hanson S. J., Cowan J. D., Giles C. L. (eds.), Morgan Kauffman Publishers, San Mateo, California, 1993 [8] Mori S., Ching Y. S. and Yamamoto K, "Historical Review of OCR Research and Development", in Proceedings of the IEEE, Vol. 80, No. 7, July 1992 [9] Jacobs R. A., Jordan M. I. and Barto A. G., "Task Decomposition through Competition in a Modular Connectionist Architecture: the What and Where Vision Tasks", COINS Technical Report No , Dept. of Computer & Information Science, University of Massachusetts, 1990 [10] Costa M., "Sulle Topologie di Connessioni in Percettroni Multistrato che Consentono di Inizializzare a Zero i Pesi della Rete", Internal Report No. DE , Dept. of Electronics, Politecnico di Torino, 1993 [11] Vogl T. P., Mangis J. K., Rigler A. K., Zink W. T. and Alkon D. L., "Accelerating the Convergence of the Back-Propagation Method", in Byol. Cybern. 59, 1988 [12] Vassallo G., Gioiello M., Condemi C. and Sorbello F., "Neural Solutions for the Handwritten Character Recognition Task", submitted to Conference on Computer Vision and Pattern Recognition, Seattle, Washington, 1994 [13] Boser B. E., Sackinger E., Bromley J., LeCun Y. and Jackel L. D., "Hardware Requirements for Neural Network Pattern Classifiers", in IEEE Micro, February 1992 [14] Reyneri L. M. and Filippi E., "An Analysis on the Performance of Silicon Implementations of Backpropagation Algorithms for Artificial Neural Networks", in IEEE Transactions on Computers, Vol. 4, No. 12, December 1991 [15] Vittoz E., "Analog VLSI Implementation of Neural Networks", in Proc. of Journées d'électronique - Réseaux de Neurones Artificiels / Artificial Neural Networks, Lausanne, Switzerland, 1989
Multi-Layer Perceptron ensembles for. increased performance and fault-tolerance in. pattern recognition tasks. E. Filippi, M. Costa, E.
Multi-Layer Perceptron ensembles for increased performance and fault-tolerance in pattern recognition tasks E. Filippi, M. Costa, E.Pasero Dipartimento di Elettronica, Politecnico di Torino C.so Duca Degli
More informationAn Hybrid MLP-SVM Handwritten Digit Recognizer
An Hybrid MLP-SVM Handwritten Digit Recognizer A. Bellili ½ ¾ M. Gilloux ¾ P. Gallinari ½ ½ LIP6, Université Pierre et Marie Curie ¾ La Poste 4, Place Jussieu 10, rue de l Ile Mabon, BP 86334 75252 Paris
More informationThe Automatic Classification Problem. Perceptrons, SVMs, and Friends: Some Discriminative Models for Classification
Perceptrons, SVMs, and Friends: Some Discriminative Models for Classification Parallel to AIMA 8., 8., 8.6.3, 8.9 The Automatic Classification Problem Assign object/event or sequence of objects/events
More informationFigure 1. Artificial Neural Network structure. B. Spiking Neural Networks Spiking Neural networks (SNNs) fall into the third generation of neural netw
Review Analysis of Pattern Recognition by Neural Network Soni Chaturvedi A.A.Khurshid Meftah Boudjelal Electronics & Comm Engg Electronics & Comm Engg Dept. of Computer Science P.I.E.T, Nagpur RCOEM, Nagpur
More informationSystolic modular VLSI Architecture for Multi-Model Neural Network Implementation +
Systolic modular VLSI Architecture for Multi-Model Neural Network Implementation + J.M. Moreno *, J. Madrenas, J. Cabestany Departament d'enginyeria Electrònica Universitat Politècnica de Catalunya Barcelona,
More informationA comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
Proc. National Conference on Recent Trends in Intelligent Computing (2006) 86-92 A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
More informationMINE 432 Industrial Automation and Robotics
MINE 432 Industrial Automation and Robotics Part 3, Lecture 5 Overview of Artificial Neural Networks A. Farzanegan (Visiting Associate Professor) Fall 2014 Norman B. Keevil Institute of Mining Engineering
More informationConvolutional Networks for Images, Speech, and. Time-Series. 101 Crawfords Corner Road Operationnelle, Universite de Montreal,
Convolutional Networks for Images, Speech, and Time-Series Yann LeCun Rm 4G332, AT&T Bell Laboratories Yoshua Bengio Dept. Informatique et Recherche 101 Crawfords Corner Road Operationnelle, Universite
More informationEnhanced MLP Input-Output Mapping for Degraded Pattern Recognition
Enhanced MLP Input-Output Mapping for Degraded Pattern Recognition Shigueo Nomura and José Ricardo Gonçalves Manzan Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, MG,
More informationBeamforming and Synchronization Algorithms Integration for OFDM HAP-Based Communications
Beamforming and Synchronization Algorithms Integration for OFDM HAP-Based Communications Daniele Borio, 1 Laura Camoriano, 2 Letizia Lo Presti, 1,3 and Marina Mondin 1,3 High Altitude Platforms (HAPs)
More informationConvolutional Networks for Images, Speech, and. Time-Series. 101 Crawfords Corner Road Operationnelle, Universite de Montreal,
Convolutional Networks for Images, Speech, and Time-Series Yann LeCun Rm 4G332, AT&T Bell Laboratories Yoshua Bengio Dept. Informatique et Recherche 101 Crawfords Corner Road Operationnelle, Universite
More informationArtificial Neural Networks. Artificial Intelligence Santa Clara, 2016
Artificial Neural Networks Artificial Intelligence Santa Clara, 2016 Simulate the functioning of the brain Can simulate actual neurons: Computational neuroscience Can introduce simplified neurons: Neural
More informationTransactions on Information and Communications Technologies vol 1, 1993 WIT Press, ISSN
Combining multi-layer perceptrons with heuristics for reliable control chart pattern classification D.T. Pham & E. Oztemel Intelligent Systems Research Laboratory, School of Electrical, Electronic and
More informationNeural Labyrinth Robot Finding the Best Way in a Connectionist Fashion
Neural Labyrinth Robot Finding the Best Way in a Connectionist Fashion Marvin Oliver Schneider 1, João Luís Garcia Rosa 1 1 Mestrado em Sistemas de Computação Pontifícia Universidade Católica de Campinas
More informationNumber Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
J Inf Process Syst, Vol.12, No.1, pp.100~108, March 2016 http://dx.doi.org/10.3745/jips.04.0022 ISSN 1976-913X (Print) ISSN 2092-805X (Electronic) Number Plate Detection with a Multi-Convolutional Neural
More informationA Divide-and-Conquer Approach to Evolvable Hardware
A Divide-and-Conquer Approach to Evolvable Hardware Jim Torresen Department of Informatics, University of Oslo, PO Box 1080 Blindern N-0316 Oslo, Norway E-mail: jimtoer@idi.ntnu.no Abstract. Evolvable
More informationDESIGN AND IMPLEMENTATION OF AN ALGORITHM FOR MODULATION IDENTIFICATION OF ANALOG AND DIGITAL SIGNALS
DESIGN AND IMPLEMENTATION OF AN ALGORITHM FOR MODULATION IDENTIFICATION OF ANALOG AND DIGITAL SIGNALS John Yong Jia Chen (Department of Electrical Engineering, San José State University, San José, California,
More informationCHAPTER 6 BACK PROPAGATED ARTIFICIAL NEURAL NETWORK TRAINED ARHF
95 CHAPTER 6 BACK PROPAGATED ARTIFICIAL NEURAL NETWORK TRAINED ARHF 6.1 INTRODUCTION An artificial neural network (ANN) is an information processing model that is inspired by biological nervous systems
More informationAnalysis of Learning Paradigms and Prediction Accuracy using Artificial Neural Network Models
Analysis of Learning Paradigms and Prediction Accuracy using Artificial Neural Network Models Poornashankar 1 and V.P. Pawar 2 Abstract: The proposed work is related to prediction of tumor growth through
More informationNEURALNETWORK BASED CLASSIFICATION OF LASER-DOPPLER FLOWMETRY SIGNALS
NEURALNETWORK BASED CLASSIFICATION OF LASER-DOPPLER FLOWMETRY SIGNALS N. G. Panagiotidis, A. Delopoulos and S. D. Kollias National Technical University of Athens Department of Electrical and Computer Engineering
More information10mW CMOS Retina and Classifier for Handheld, 1000Images/s Optical Character Recognition System
TP 12.1 10mW CMOS Retina and Classifier for Handheld, 1000Images/s Optical Character Recognition System Peter Masa, Pascal Heim, Edo Franzi, Xavier Arreguit, Friedrich Heitger, Pierre Francois Ruedi, Pascal
More informationMULTI-MODULAR ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR ONLINE HANDWRITTEN CHARACTER RECOGNITION
MULTI-MODULAR ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR ONLINE HANDWRITTEN CHARACTER RECOGNITION Emilie POISSON*, Christian VIARD GAUDIN*, Pierre-Michel LALLICAN** * Image Video Communication,
More informationNeural Network Classifier and Filtering for EEG Detection in Brain-Computer Interface Device
Neural Network Classifier and Filtering for EEG Detection in Brain-Computer Interface Device Mr. CHOI NANG SO Email: cnso@excite.com Prof. J GODFREY LUCAS Email: jglucas@optusnet.com.au SCHOOL OF MECHATRONICS,
More informationFACE RECOGNITION USING NEURAL NETWORKS
Int. J. Elec&Electr.Eng&Telecoms. 2014 Vinoda Yaragatti and Bhaskar B, 2014 Research Paper ISSN 2319 2518 www.ijeetc.com Vol. 3, No. 3, July 2014 2014 IJEETC. All Rights Reserved FACE RECOGNITION USING
More informationA Neural Network Facial Expression Recognition System using Unsupervised Local Processing
A Neural Network Facial Expression Recognition System using Unsupervised Local Processing Leonardo Franco Alessandro Treves Cognitive Neuroscience Sector - SISSA 2-4 Via Beirut, Trieste, 34014 Italy lfranco@sissa.it,
More informationMLP for Adaptive Postprocessing Block-Coded Images
1450 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 10, NO. 8, DECEMBER 2000 MLP for Adaptive Postprocessing Block-Coded Images Guoping Qiu, Member, IEEE Abstract A new technique
More informationMULTIPLE CLASSIFIERS FOR ELECTRONIC NOSE DATA
MULTIPLE CLASSIFIERS FOR ELECTRONIC NOSE DATA M. Pardo, G. Sberveglieri INFM and University of Brescia Gas Sensor Lab, Dept. of Chemistry and Physics for Materials Via Valotti 9-25133 Brescia Italy D.
More informationDisruption Classification at JET with Neural Techniques
EFDA JET CP(03)01-65 M. K. Zedda, T. Bolzonella, B. Cannas, A. Fanni, D. Howell, M. F. Johnson, P. Sonato and JET EFDA Contributors Disruption Classification at JET with Neural Techniques . Disruption
More informationNEURAL NETWORK BASED MAXIMUM POWER POINT TRACKING
NEURAL NETWORK BASED MAXIMUM POWER POINT TRACKING 3.1 Introduction This chapter introduces concept of neural networks, it also deals with a novel approach to track the maximum power continuously from PV
More informationAgent Smith: An Application of Neural Networks to Directing Intelligent Agents in a Game Environment
Agent Smith: An Application of Neural Networks to Directing Intelligent Agents in a Game Environment Jonathan Wolf Tyler Haugen Dr. Antonette Logar South Dakota School of Mines and Technology Math and
More informationA Novel Fuzzy Neural Network Based Distance Relaying Scheme
902 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 3, JULY 2000 A Novel Fuzzy Neural Network Based Distance Relaying Scheme P. K. Dash, A. K. Pradhan, and G. Panda Abstract This paper presents a new
More informationFINANCIAL TIME SERIES FORECASTING USING A HYBRID NEURAL- EVOLUTIVE APPROACH
FINANCIAL TIME SERIES FORECASTING USING A HYBRID NEURAL- EVOLUTIVE APPROACH JUAN J. FLORES 1, ROBERTO LOAEZA 1, HECTOR RODRIGUEZ 1, FEDERICO GONZALEZ 2, BEATRIZ FLORES 2, ANTONIO TERCEÑO GÓMEZ 3 1 Division
More informationTarget detection in side-scan sonar images: expert fusion reduces false alarms
Target detection in side-scan sonar images: expert fusion reduces false alarms Nicola Neretti, Nathan Intrator and Quyen Huynh Abstract We integrate several key components of a pattern recognition system
More informationApplication of Artificial Neural Networks System for Synthesis of Phased Cylindrical Arc Antenna Arrays
International Journal of Communication Engineering and Technology. ISSN 2277-3150 Volume 4, Number 1 (2014), pp. 7-15 Research India Publications http://www.ripublication.com Application of Artificial
More informationApplication of Multi Layer Perceptron (MLP) for Shower Size Prediction
Chapter 3 Application of Multi Layer Perceptron (MLP) for Shower Size Prediction 3.1 Basic considerations of the ANN Artificial Neural Network (ANN)s are non- parametric prediction tools that can be used
More informationImage Extraction using Image Mining Technique
IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 9 (September. 2013), V2 PP 36-42 Image Extraction using Image Mining Technique Prof. Samir Kumar Bandyopadhyay,
More informationPrediction of Cluster System Load Using Artificial Neural Networks
Prediction of Cluster System Load Using Artificial Neural Networks Y.S. Artamonov 1 1 Samara National Research University, 34 Moskovskoe Shosse, 443086, Samara, Russia Abstract Currently, a wide range
More informationAuthor(s) Corr, Philip J.; Silvestre, Guenole C.; Bleakley, Christopher J. The Irish Pattern Recognition & Classification Society
Provided by the author(s) and University College Dublin Library in accordance with publisher policies. Please cite the published version when available. Title Open Source Dataset and Deep Learning Models
More informationArtificial Intelligence: Using Neural Networks for Image Recognition
Kankanahalli 1 Sri Kankanahalli Natalie Kelly Independent Research 12 February 2010 Artificial Intelligence: Using Neural Networks for Image Recognition Abstract: The engineering goals of this experiment
More informationNeural networks are very
How Not to Be Frustrated with Neural Networks BOGDAN M. WILAMOWSKI Neural networks are very powerful as nonlinear signal processors, but obtained results are often far from satisfactory. The purpose of
More informationIMPLEMENTATION OF NEURAL NETWORK IN ENERGY SAVING OF INDUCTION MOTOR DRIVES WITH INDIRECT VECTOR CONTROL
IMPLEMENTATION OF NEURAL NETWORK IN ENERGY SAVING OF INDUCTION MOTOR DRIVES WITH INDIRECT VECTOR CONTROL * A. K. Sharma, ** R. A. Gupta, and *** Laxmi Srivastava * Department of Electrical Engineering,
More informationAn Evolutionary Approach to the Synthesis of Combinational Circuits
An Evolutionary Approach to the Synthesis of Combinational Circuits Cecília Reis Institute of Engineering of Porto Polytechnic Institute of Porto Rua Dr. António Bernardino de Almeida, 4200-072 Porto Portugal
More informationPolitecnico di Torino. Porto Institutional Repository
Politecnico di Torino Porto Institutional Repository [Proceeding] Integrated miniaturized antennas for automotive applications Original Citation: Vietti G., Dassano G., Orefice M. (2010). Integrated miniaturized
More informationAn Optimized Wallace Tree Multiplier using Parallel Prefix Han-Carlson Adder for DSP Processors
An Optimized Wallace Tree Multiplier using Parallel Prefix Han-Carlson Adder for DSP Processors T.N.Priyatharshne Prof. L. Raja, M.E, (Ph.D) A. Vinodhini ME VLSI DESIGN Professor, ECE DEPT ME VLSI DESIGN
More information(x, y ) x = (a, b, c, d, x, y )
Face Detection with Neural Networks Jacob H. Stríom Department of Electrical and Computer Engineering University of California, San Diego San Diego, California Abstract A method for ænding faces in images
More informationANALYSIS OF JPEG2000 QUALITY IN PHOTOGRAMMETRIC APPLICATIONS
ANALYSIS OF 2000 QUALITY IN PHOTOGRAMMETRIC APPLICATIONS A. Biasion, A. Lingua, F. Rinaudo DITAG, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ITALY andrea.biasion@polito.it, andrea.lingua@polito.it,
More information258 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 33, NO. 2, APRIL 2003
258 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 33, NO. 2, APRIL 2003 Genetic Design of Biologically Inspired Receptive Fields for Neural Pattern Recognition Claudio A.
More informationA Fast Segmentation Algorithm for Bi-Level Image Compression using JBIG2
A Fast Segmentation Algorithm for Bi-Level Image Compression using JBIG2 Dave A. D. Tompkins and Faouzi Kossentini Signal Processing and Multimedia Group Department of Electrical and Computer Engineering
More informationCurrent Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies
Journal of Electrical Engineering 5 (27) 29-23 doi:.7265/2328-2223/27.5. D DAVID PUBLISHING Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Patrice Wira and Thien Minh Nguyen
More informationNeural Network with Median Filter for Image Noise Reduction
Available online at www.sciencedirect.com IERI Procedia 00 (2012) 000 000 2012 International Conference on Mechatronic Systems and Materials Neural Network with Median Filter for Image Noise Reduction
More informationSegmentation of Fingerprint Images
Segmentation of Fingerprint Images Asker M. Bazen and Sabih H. Gerez University of Twente, Department of Electrical Engineering, Laboratory of Signals and Systems, P.O. box 217-75 AE Enschede - The Netherlands
More informationSMARTPHONE SENSOR BASED GESTURE RECOGNITION LIBRARY
SMARTPHONE SENSOR BASED GESTURE RECOGNITION LIBRARY Sidhesh Badrinarayan 1, Saurabh Abhale 2 1,2 Department of Information Technology, Pune Institute of Computer Technology, Pune, India ABSTRACT: Gestures
More informationAN EFFICIENT APPROACH FOR VISION INSPECTION OF IC CHIPS LIEW KOK WAH
AN EFFICIENT APPROACH FOR VISION INSPECTION OF IC CHIPS LIEW KOK WAH Report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Computer Systems & Software Engineering
More informationBiologically Inspired Computation
Biologically Inspired Computation Deep Learning & Convolutional Neural Networks Joe Marino biologically inspired computation biological intelligence flexible capable of detecting/ executing/reasoning about
More informationTCM-coded OFDM assisted by ANN in Wireless Channels
1 Aradhana Misra & 2 Kandarpa Kumar Sarma Dept. of Electronics and Communication Technology Gauhati University Guwahati-781014. Assam, India Email: aradhana66@yahoo.co.in, kandarpaks@gmail.com Abstract
More informationSynthesis of Fault Tolerant Neural Networks
Synthesis of Fault Tolerant Neural Networks Dhananjay S. Phatak and Elko Tchernev ABSTRACT This paper evaluates different strategies for enhancing (partial) fault tolerance (PFT) of feedforward artificial
More informationResearch on Hand Gesture Recognition Using Convolutional Neural Network
Research on Hand Gesture Recognition Using Convolutional Neural Network Tian Zhaoyang a, Cheng Lee Lung b a Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China E-mail address:
More informationBackground Pixel Classification for Motion Detection in Video Image Sequences
Background Pixel Classification for Motion Detection in Video Image Sequences P. Gil-Jiménez, S. Maldonado-Bascón, R. Gil-Pita, and H. Gómez-Moreno Dpto. de Teoría de la señal y Comunicaciones. Universidad
More informationAN IMPROVED NEURAL NETWORK-BASED DECODER SCHEME FOR SYSTEMATIC CONVOLUTIONAL CODE. A Thesis by. Andrew J. Zerngast
AN IMPROVED NEURAL NETWORK-BASED DECODER SCHEME FOR SYSTEMATIC CONVOLUTIONAL CODE A Thesis by Andrew J. Zerngast Bachelor of Science, Wichita State University, 2008 Submitted to the Department of Electrical
More informationStochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines
Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines Roman Genov and Gert Cauwenberghs Department of Electrical and Computer Engineering Johns Hopkins University, Baltimore, MD
More informationUSING EMBEDDED PROCESSORS IN HARDWARE MODELS OF ARTIFICIAL NEURAL NETWORKS
USING EMBEDDED PROCESSORS IN HARDWARE MODELS OF ARTIFICIAL NEURAL NETWORKS DENIS F. WOLF, ROSELI A. F. ROMERO, EDUARDO MARQUES Universidade de São Paulo Instituto de Ciências Matemáticas e de Computação
More informationA COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND OTHER STATISTICAL METHODS FOR ROTATING MACHINE
A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND OTHER STATISTICAL METHODS FOR ROTATING MACHINE CONDITION CLASSIFICATION A. C. McCormick and A. K. Nandi Abstract Statistical estimates of vibration signals
More informationARTIFICIAL NEURAL NETWORK BASED CLASSIFICATION FOR MONOBLOCK CENTRIFUGAL PUMP USING WAVELET ANALYSIS
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6340(Print) ISSN 0976 6359(Online) Volume 1 Number 1, July - Aug (2010), pp. 28-37 IAEME, http://www.iaeme.com/ijmet.html
More informationDERIVATION OF TRAPS IN AUDITORY DOMAIN
DERIVATION OF TRAPS IN AUDITORY DOMAIN Petr Motlíček, Doctoral Degree Programme (4) Dept. of Computer Graphics and Multimedia, FIT, BUT E-mail: motlicek@fit.vutbr.cz Supervised by: Dr. Jan Černocký, Prof.
More informationESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS
ESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS R. Bolla, F. Davoli, A. Giordano Department of Communications, Computer and Systems Science (DIST University of Genoa Via Opera Pia 13, I-115
More informationArtificial Neural Networks
Artificial Neural Networks ABSTRACT Just as life attempts to understand itself better by modeling it, and in the process create something new, so Neural computing is an attempt at modeling the workings
More informationPose Invariant Face Recognition
Pose Invariant Face Recognition Fu Jie Huang Zhihua Zhou Hong-Jiang Zhang Tsuhan Chen Electrical and Computer Engineering Department Carnegie Mellon University jhuangfu@cmu.edu State Key Lab for Novel
More informationA Novel Morphological Method for Detection and Recognition of Vehicle License Plates
American Journal of Applied Sciences 6 (12): 2066-2070, 2009 ISSN 1546-9239 2009 Science Publications A Novel Morphological Method for Detection and Recognition of Vehicle License Plates 1 S.H. Mohades
More informationDC Motor Speed Control using Artificial Neural Network
International Journal of Modern Communication Technologies & Research (IJMCTR) ISSN: 2321-0850, Volume-2, Issue-2, February 2014 DC Motor Speed Control using Artificial Neural Network Yogesh, Swati Gupta,
More informationA Reinforcement Learning Scheme for Adaptive Link Allocation in ATM Networks
A Reinforcement Learning Scheme for Adaptive Link Allocation in ATM Networks Ernst Nordström, Jakob Carlström Department of Computer Systems, Uppsala University, Box 325, S 751 05 Uppsala, Sweden Fax:
More informationDynamic Programming. Objective
Dynamic Programming Richard de Neufville Professor of Engineering Systems and of Civil and Environmental Engineering MIT Massachusetts Institute of Technology Dynamic Programming Slide 1 of 43 Objective
More informationThe Use of Neural Network to Recognize the Parts of the Computer Motherboard
Journal of Computer Sciences 1 (4 ): 477-481, 2005 ISSN 1549-3636 Science Publications, 2005 The Use of Neural Network to Recognize the Parts of the Computer Motherboard Abbas M. Ali, S.D.Gore and Musaab
More informationTransactions on Information and Communications Technologies vol 6, 1994 WIT Press, ISSN
Application of artificial neural networks to the robot path planning problem P. Martin & A.P. del Pobil Department of Computer Science, Jaume I University, Campus de Penyeta Roja, 207 Castellon, Spain
More informationMulti-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines
Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines ROBINEL Audrey & PUZENAT Didier {arobinel, dpuzenat}@univ-ag.fr Laboratoire
More informationLive Hand Gesture Recognition using an Android Device
Live Hand Gesture Recognition using an Android Device Mr. Yogesh B. Dongare Department of Computer Engineering. G.H.Raisoni College of Engineering and Management, Ahmednagar. Email- yogesh.dongare05@gmail.com
More informationMehrdad Amirghasemi a* Reza Zamani a
The roles of evolutionary computation, fitness landscape, constructive methods and local searches in the development of adaptive systems for infrastructure planning Mehrdad Amirghasemi a* Reza Zamani a
More informationPrediction of airblast loads in complex environments using artificial neural networks
Structures Under Shock and Impact IX 269 Prediction of airblast loads in complex environments using artificial neural networks A. M. Remennikov 1 & P. A. Mendis 2 1 School of Civil, Mining and Environmental
More informationProduct architecture and the organisation of industry. The role of firm competitive behaviour
Product architecture and the organisation of industry. The role of firm competitive behaviour Tommaso Ciarli Riccardo Leoncini Sandro Montresor Marco Valente October 19, 2009 Abstract submitted to the
More informationTHERMAL DETECTION OF WATER SATURATION SPOTS FOR LANDSLIDE PREDICTION
THERMAL DETECTION OF WATER SATURATION SPOTS FOR LANDSLIDE PREDICTION Aufa Zin, Kamarul Hawari and Norliana Khamisan Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan,
More informationImprovement of Classical Wavelet Network over ANN in Image Compression
International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P), Volume-7, Issue-5, May 2017 Improvement of Classical Wavelet Network over ANN in Image Compression
More informationStatistical Tests: More Complicated Discriminants
03/07/07 PHY310: Statistical Data Analysis 1 PHY310: Lecture 14 Statistical Tests: More Complicated Discriminants Road Map When the likelihood discriminant will fail The Multi Layer Perceptron discriminant
More informationarxiv: v3 [cs.cv] 18 Dec 2018
Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth Ankur Singh 1 Anurag Chanani 2 Harish Karnick 3 arxiv:1812.03858v3 [cs.cv] 18 Dec 2018 Abstract In this paper,
More informationHandwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)
Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON) Parveen Kumar Department of E.C.E Lecturer, NCCE Israna Nitin Sharma Department of E.C.E
More informationAN ERROR LIMITED AREA EFFICIENT TRUNCATED MULTIPLIER FOR IMAGE COMPRESSION
AN ERROR LIMITED AREA EFFICIENT TRUNCATED MULTIPLIER FOR IMAGE COMPRESSION K.Mahesh #1, M.Pushpalatha *2 #1 M.Phil.,(Scholar), Padmavani Arts and Science College. *2 Assistant Professor, Padmavani Arts
More informationInitialisation improvement in engineering feedforward ANN models.
Initialisation improvement in engineering feedforward ANN models. A. Krimpenis and G.-C. Vosniakos National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Division,
More informationA VLSI Convolutional Neural Network for Image Recognition Using Merged/Mixed Analog-Digital Architecture
A VLSI Convolutional Neural Network for Image Recognition Using Merged/Mixed Analog-Digital Architecture Keisuke Korekado a, Takashi Morie a, Osamu Nomura b, Hiroshi Ando c, Teppei Nakano a, Masakazu Matsugu
More informationIntroduction to Machine Learning
Introduction to Machine Learning Deep Learning Barnabás Póczos Credits Many of the pictures, results, and other materials are taken from: Ruslan Salakhutdinov Joshua Bengio Geoffrey Hinton Yann LeCun 2
More informationAutomatic Vehicles Detection from High Resolution Satellite Imagery Using Morphological Neural Networks
Automatic Vehicles Detection from High Resolution Satellite Imagery Using Morphological Neural Networks HONG ZHENG Research Center for Intelligent Image Processing and Analysis School of Electronic Information
More informationIndustrial computer vision using undefined feature extraction
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 1995 Industrial computer vision using undefined feature extraction Phil
More informationCSC384 Intro to Artificial Intelligence* *The following slides are based on Fahiem Bacchus course lecture notes.
CSC384 Intro to Artificial Intelligence* *The following slides are based on Fahiem Bacchus course lecture notes. Artificial Intelligence A branch of Computer Science. Examines how we can achieve intelligent
More informationMURDOCH RESEARCH REPOSITORY
MURDOCH RESEARCH REPOSITORY http://dx.doi.org/10.1109/asspcc.2000.882494 Jan, T., Zaknich, A. and Attikiouzel, Y. (2000) Separation of signals with overlapping spectra using signal characterisation and
More informationIntroduction to Machine Learning
Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial
More informationMATLAB/GUI Simulation Tool for Power System Fault Analysis with Neural Network Fault Classifier
MATLAB/GUI Simulation Tool for Power System Fault Analysis with Neural Network Fault Classifier Ph Chitaranjan Sharma, Ishaan Pandiya, Dipak Swargari, Kusum Dangi * Department of Electrical Engineering,
More informationMICROELECTRONIC IMPLEMENTATIONS OF CONNECTIONIST NEURAL NETWORKS
515 MICROELECTRONIC IMPLEMENTATIONS OF CONNECTIONIST NEURAL NETWORKS Stuart Mackie, Hans P. Graf, Daniel B. Schwartz, and John S. Denker AT&T Bell Labs, Holmdel, NJ 07733 Abstract In this paper we discuss
More informationMAGNT Research Report (ISSN ) Vol.6(1). PP , Controlling Cost and Time of Construction Projects Using Neural Network
Controlling Cost and Time of Construction Projects Using Neural Network Li Ping Lo Faculty of Computer Science and Engineering Beijing University China Abstract In order to achieve optimized management,
More informationThe Basic Kak Neural Network with Complex Inputs
The Basic Kak Neural Network with Complex Inputs Pritam Rajagopal The Kak family of neural networks [3-6,2] is able to learn patterns quickly, and this speed of learning can be a decisive advantage over
More informationAn Approach to Detect QRS Complex Using Backpropagation Neural Network
An Approach to Detect QRS Complex Using Backpropagation Neural Network MAMUN B.I. REAZ 1, MUHAMMAD I. IBRAHIMY 2 and ROSMINAZUIN A. RAHIM 2 1 Faculty of Engineering, Multimedia University, 63100 Cyberjaya,
More informationUsing RASTA in task independent TANDEM feature extraction
R E S E A R C H R E P O R T I D I A P Using RASTA in task independent TANDEM feature extraction Guillermo Aradilla a John Dines a Sunil Sivadas a b IDIAP RR 04-22 April 2004 D a l l e M o l l e I n s t
More informationHybrid of Evolution and Reinforcement Learning for Othello Players
Hybrid of Evolution and Reinforcement Learning for Othello Players Kyung-Joong Kim, Heejin Choi and Sung-Bae Cho Dept. of Computer Science, Yonsei University 134 Shinchon-dong, Sudaemoon-ku, Seoul 12-749,
More informationA Simple Design and Implementation of Reconfigurable Neural Networks
A Simple Design and Implementation of Reconfigurable Neural Networks Hazem M. El-Bakry, and Nikos Mastorakis Abstract There are some problems in hardware implementation of digital combinational circuits.
More information