EvoNF: A Framework for Optimization of Fuzzy Inference Systems Using Neural Network Learning and Evolutionary Computation

TL;DR

This paper introduces EvoNF, a hybrid framework combining neural network learning and evolutionary algorithms to optimize fuzzy inference systems more effectively than existing methods.

Contribution

It proposes a novel integrated methodology that combines neural networks, fuzzy inference, and evolutionary search to enhance optimization performance.

Findings

Demonstrates improved optimization efficiency through experiments

Shows better convergence compared to traditional neuro-fuzzy models

Validates the approach with theoretical and experimental results

Abstract

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy inference system will be successful. Success of evolutionary search procedures for optimization of fuzzy inference system is well proven and established in many application areas. In this paper, we will explore how the optimization of fuzzy inference systems could be further improved using a meta-heuristic approach combining neural network learning and evolutionary computation. The proposed technique could be considered as a methodology to integrate neural networks, fuzzy inference systems and evolutionary search procedures. We present the theoretical frameworks and some experimental results to demonstrate the efficiency of the proposed technique.