Proceedings

An Effective Learning Method for Max-Min Neural Networks

Loo-Nin Teow, Kia-Fock Loe

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Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Neural Networks. Pages 1134-1138.

Abstract

Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-v<~.lued 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 differentiable, 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-min 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.

Loo-Nin Teow, and Kia-Fock Loe. 1997. An Effective Learning Method for Max-Min Neural Networks. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97. IJCAI, 1134–1138. https://ijcai.org/proceedings/1997/164
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