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Neural methods for obtaining fuzzy rules.

José Manuel Benítez;  Armando Blanco;  Miguel Delgado;  Ignacio Requena — 1996

Mathware and Soft Computing

In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.

New aspects on extraction of fuzzy rules using neural networks.

José Manuel Benítez;  Armando Blanco;  Miguel Delgado;  Ignacio Requena — 1998

Mathware and Soft Computing

In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the Backpropagation algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an adaptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the assignement...

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