Fuzzy self-organizing hybrid neural network for gas analysis system

Author

Osowski, Stanislaw ; Brudzewski, Kazimierz

Author_Institution

Inst. of the Theory of Electr. Eng. & Electr. Meas., Warsaw Univ. of Technol., Poland

Volume

49

Issue

2

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

424

Lastpage

428

Abstract

The paper presents the gas analysis system applying the self-organizing fuzzy hybrid neural network. The network is composed of the self-organizing competitive fuzzy layer and the supervised multilayer perceptron (MLP) subnetwork, connected in cascade. The characteristic features of this network structure for gas analysis systems are discussed and the results of experiments compared to standard neural solutions based on MLP or classical hybrid network employing the Kohonen layer

Keywords

air pollution measurement; array signal processing; chemical engineering computing; feature extraction; feedforward neural nets; fuzzy neural nets; gas sensors; learning (artificial intelligence); multilayer perceptrons; pattern clustering; self-organising feature maps; cascade connected; feature extraction; fuzzy self-organizing hybrid neural network; gas analysis system; gas pollutants recognition; learning patterns; mean absolute error; pattern clustering; self-organizing competitive fuzzy layer; semiconductor oxide gas sensors; sensor array; signal processing; supervised multilayer perceptron subnetwork; Fuzzy neural networks; Fuzzy systems; Gas detectors; Gases; Multilayer perceptrons; Neural networks; Neurons; Pollution measurement; Sensor arrays; Signal processing;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.843090

Filename

843090