A dual network adaptive learning algorithm for supervised neural network with contour preserving classification for soft real time applications

Authors:
Piyabute Fuangkhon;Thitipong Tanprasert
Affiliations:
Distributed and Parallel Computing Research Laboratory, Assumption University, Bangkok;Distributed and Parallel Computing Research Laboratory, Assumption University, Bangkok
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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Abstract

A framework presenting a basic conceptual structure used to solve adaptive learning problems in soft real time applications is proposed Its design consists of two supervised neural networks running simultaneously One is used for training data and the other is used for testing data The accuracy of the classification is improved from the previous works by adding outpost vectors generated from prior samples The testing function is able to test data continuously without being interrupted while the training function is being executed The framework is designed for a parallel processing and/or a distributed processing environment due to the highly demanded processing power of the repetitive training process of the neural network.