Data-dependent filters with fuzzy-neural network

Author

Taguchi, Akira ; Takashima, Hironori

Author_Institution

Dept. of Electr. & Electron. Eng., Musashi Inst. of Technol., Tokyo, Japan

Volume

1

fYear

1995

fDate

Nov/Dec 1995

Firstpage

584

Abstract

This paper presents a design method of data-dependent filters by using fuzzy inference for the purpose of restoring signals degraded by additive noise. Since the antecedents of fuzzy inference can be composed of many local characteristics, it is possible for the proposed filter to adjust its weights to adapt to local data in input signal. The proposed filter achieve maximum noise reduction in uniform areas and preserve details of input signals as well. Furthermore, the proposed filter can be constructed by fuzzy neural networks, and so the tuning of this results in backpropagation algorithm

Keywords

adaptive filters; backpropagation; filtering theory; fuzzy neural nets; signal restoration; backpropagation; data-dependent filters; fuzzy inference; fuzzy-neural network; noise reduction; signal restoration; Adaptive filters; Additive noise; Backpropagation algorithms; Data engineering; Degradation; Design engineering; Design methodology; Electronic mail; Filtering; Filters; Fuzzy neural networks; Fuzzy sets; Noise reduction; Signal processing algorithms; Signal restoration; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.488244

Filename

488244