Improvement of precision of neuro-fuzzy system by increase of activation of rules

Abstract

Neuro-fuzzy systems have proved to be a powerful tool for data approximation and generalization. A rule base is a crucial part of a neuro-fuzzy system. The data items activate the rules and their answers are aggregated into a final answer. The experiments reveal that sometimes the activation of all rules in a rule base is very low. It means the system recognizes the data items very poorly. The paper presents a modification of the neuro-fuzzy system: the tuning procedure has two objectives: minimizing of the error of the system and maximizing of the activation of rules. The higher activation (better recognition of the data items) makes the model more reliable. The increase of the activation of rules may also decrease the error rate for the model. The paper is accompanied by the numerical examples.