HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES-Reference-Cited by-同舟云学术

HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES

Published:2005-02 Issue:01n02 Volume:15 Page:137-149
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

ZANCHETTIN CLEBER¹,LUDERMIR TERESA B.¹

Affiliation:

1. CIn — Federal University of Pernambuco, Cidade Universitaria, Recife — PE, P. O. Box 7851, 50.732-970, Brazil

Abstract

This work examines the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The connectionist approaches Multi-Layer Perceptron and Time Delay Neural Networks, and the hybrid approaches Feature-Weighted Detector and Evolving Neural Fuzzy Networks were investigated. A Wavelet Filter is evaluated as a preprocessing method for odor signals. The signals generated by an artificial nose were composed by an array of conducting polymer sensors and exposed to two different odor databases.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065705000141

Reference21 articles.

1. L. R. Medsker, Hybrid Intelligent Systems (Kluwer, Academic Publishers) p. 312.

2. Learning representations by back-propagating errors

3. A fuzzy neural network for pattern classification and feature selection

4. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning

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