A Method for the Improvement of the Behavior of Bidirectional Associative Memories as Pattern Classifiers

Authors:
Francisco J. López-Aligué;Ignacio Alvarez-Troncoso;M. Isabel Acevedo-Sotoca;Carlos J. García-Orellana;Miguel Macías-Macías
Affiliations:
Department of Electronics and Electromechanical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n. 06007 Badajoz, Spain;Department of Electronics and Electromechanical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n. 06007 Badajoz, Spain;Department of Electronics and Electromechanical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n. 06007 Badajoz, Spain;Department of Electronics and Electromechanical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n. 06007 Badajoz, Spain;Department of Electronics and Electromechanical Engineering, Faculty of Sciences, University of Extremadura, Avda. de Elvas, s/n. 06007 Badajoz, Spain
Venue:
Neural Processing Letters
Year:
2003

Citing 9
Cited 0

Bidirectional associative memories

IEEE Transactions on Systems, Man and Cybernetics
Multiple training concept for back-propagation neural networks for use in associative memories

Neural Networks
A synthesis procedure for associative memories based on space-varying cellular neural networks

Neural Networks
Adaptive Bam for Pattern Classification

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
An overview of character recognition focused on off-line handwriting

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A general model for bidirectional associative memories

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the problem of spurious patterns in neural associative memory models

IEEE Transactions on Neural Networks
A feedforward bidirectional associative memory

IEEE Transactions on Neural Networks
Memory annihilation of structured maps in bidirectional associative memories

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a form of attaining success levels of up to 100% in character classification by the appropriate use of thresholds in the activity functions of the neurons making up the two-layer network with which bidirectional associative memories are implemented, together with a systematic method for generating the weight matrix. The system that is constructed includes a geometrical pre-processing stage that eliminates distortions, thereby improving the results. As a final characteristic, the functioning of the system presents a high level of immunity to noise or deformations. The system was evaluated using the two popular databases NIST#19 and UCI. There was found to be no misclassification in any case, whether under conditions of heavy contamination from noise or distortion of the image to be classified.