Neo-fuzzy-neuron based new approach to system modeling, with application to actual system

Author

Uchino, Eiji ; Yamakawa, Takeshi

Author_Institution

Dept. of Control Eng. & Sci., Kyushu Inst. of Technol., Fukuoka, Japan

fYear

1994

fDate

6-9 Nov 1994

Firstpage

564

Lastpage

570

Abstract

This paper introduces a new approach to system modeling by using a neo-fuzzy-neuron. The system of concern is modeled adaptively by simply feeding to the neo-fuzzy-neuron, the basic principle of which was proposed by the authors in 1992, the input and the output data of the objective system. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then extended in order to get a better generalization capability. An adaptive fuzzy modeling with use of a piece-wise linear membership function is also introduced. The experimental results have provided substantial proofs for their practical use

Keywords

fuzzy logic; fuzzy neural nets; generalisation (artificial intelligence); piecewise-linear techniques; signal restoration; adaptive fuzzy modeling; chaotic signal restoration; generalization; input data; intermittent speech signal; neo-fuzzy-neuron; output data; piecewise linear membership function; saturated signal; speech signal restoration; system modeling; Application software; Chaos; Control engineering; Fuzzy logic; Fuzzy sets; Fuzzy systems; Modeling; Neural networks; Nonlinear systems; Piecewise linear techniques;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1994. Proceedings., Sixth International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-8186-6785-0

Type

conf

DOI

10.1109/TAI.1994.346442

Filename

346442