Identification of non-linear systems, based on neural networks, with applications at fuzzy systems

Authors:
Constantin Volosencu
Affiliations:
Department of Automatics and Applied Informatics, "Politehnica" University of Timisoara, Romania
Venue:
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
Year:
2009

Citing 3
Cited 2

Introduction to the theory of neural computation

Introduction to the theory of neural computation
On some properties of fuzzy systems

ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
Coin identification using neural networks

SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing

Identification of distributed parameter systems based on sensor networks and multivariable estimation techniques

SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Two step, PID and model predictive control using artificial neural network applied on semi-batch reactor

WSEAS TRANSACTIONS on SYSTEMS

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a short review how to use feedforward neural networks for non-linear system identification, with application at the neural implementation of a fuzzy system. In this application the input-output transfer characteristics of the fuzzy system are used to evaluate the accuracy of the identification results expressed for a neuro-fuzzy model. This method could be used for identification of the most general fuzzy systems, which are non-linear systems, being developed with all kind of fuzzyfication methods, rule bases, inference methods and defuzzification methods. Using this method, accurate neural models for a large class of fuzzy systems may be obtained. The neuro-fuzzy model preserves all the properties of the fuzzy systems: the values of the transfer gain and the sector property. The neuro-fuzzy model obtained by identification is useful in all applications of the fuzzy systems, for example in control. Good values of the quality neural identification criteria are obtained. The optimized neuro-fuzzy model is given by its structure, weights and biases, related to the most adequate training method.