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|Title:||An effective learning method for max-min neural networks||Authors:||Teow, L.-N.
|Issue Date:||1997||Citation:||Teow, L.-N.,Loe, K.-F. (1997). An effective learning method for max-min neural networks. IJCAI International Joint Conference on Artificial Intelligence 2 : 1134-1139. ScholarBank@NUS Repository.||Abstract:||Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiate, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max-min error functions. This method is applied to a "typical" fuzzy-neural network model employing max-rnin activation functions. We show how this network can be trained to perform function approximation; its performance was found to be better than that of a conventional feedforward neural network.||Source Title:||IJCAI International Joint Conference on Artificial Intelligence||URI:||http://scholarbank.nus.edu.sg/handle/10635/116689||ISSN:||10450823|
|Appears in Collections:||Staff Publications|
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