On automatic design of neuro-fuzzy systems

Authors:
Krzysztof Cpałka;Leszek Rutkowski;Meng Joo Er
Affiliations:
Academy of Management, SWSPiZ, Institute of Information Technology, Poland and Czestochowa University of Technology, Department of Computer Engineering, Poland;Academy of Management, SWSPiZ, Institute of Information Technology, Poland and Czestochowa University of Technology, Department of Computer Engineering, Poland;Nanyang Technological University, School of Electrical & Electronic Engineering, Singapore
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

Citing 7
Cited 0

Computational Intelligence: Methods and Techniques

Computational Intelligence: Methods and Techniques
Introduction to Genetic Algorithms

Introduction to Genetic Algorithms
A new method for design and reduction of neuro-fuzzy classification systems

IEEE Transactions on Neural Networks
Evolutionary learning of mamdani-type neuro-fuzzy systems

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A robust design criterion for interpretable fuzzy models with uncertain data

IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework

IEEE Transactions on Neural Networks
Flexible neuro-fuzzy systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a new approach for automatic design of neuro-fuzzy systems. We apply evolutionary strategy to determine the number of rules, number of antecedents, number of inputs, and number of discretization points of neuro-fuzzy systems. Proper selection of these elements influences the accuracy of the system and its interpretability. The algorithm has been tested using well-known classification benchmarks.