Title :
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
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;
Conference_Titel :
Tools with Artificial Intelligence, 1994. Proceedings., Sixth International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-8186-6785-0
DOI :
10.1109/TAI.1994.346442
