An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification

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2009-11
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eng
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10 pages
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Proceedings of the 6 th International Conference on Electrical Engineering/Electronics Computer, Telecommunication and Information Technology (ECTI-CON 2009). Pattaya, Chonburi, Thailand, (May 6-9, 2009), 740-743
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Abstract
A study of noise tolerance characteristics of an adaptive learning algorithm for supervised neural network is presented in this paper. The algorithm allows the existing knowledge to age out in slow rate as a supervised neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm utilizes the contour preserving classification algorithm to pre-process the training data to improve the classification and the noise tolerance. The experimental results convincingly confirm the effectiveness of the algorithm and the improvement of noise tolerance.
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