A CONCURRENT FUZZY NEURAL NETWORK APPROACH FOR A FUZZY GAUSSIAN NEURAL NETWORK

A CONCURRENT FUZZY NEURAL NETWORK APPROACH FOR A FUZZY GAUSSIAN NEURAL NETWORK

Iatan, I. F.

Full Article:

The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy Gaussian modules. Our proposed model will be applied for the pattern classification. The fuzzy neural model consists of a set of M fuzzy neural networks, one for every class, each network having a single output. The output value corresponding to the k, k 1,M neural network is equal to 1 for those patterns belonging to the class k and 0 for the others patterns from the training set. After we have trained the M fuzzy neural networks, we shall save the weights in M files, in order to be used in the test stage of the respective networks. We have applied this model as a classifier, in a cascade having the following processing stages: the application of a pattern to the input of each of the M networks; computing the output of the respective network and then taking the maximum of those M outputs. The results of computer simulation will be given.

The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy Gaussian modules. Our proposed model will be applied for the pattern classification. The fuzzy neural model consists of a set of M fuzzy neural networks, one for every class, each network having a single output. The output value corresponding to the k, k 1,M neural network is equal to 1 for those patterns belonging to the class k and 0 for the others patterns from the training set. After we have trained the M fuzzy neural networks, we shall save the weights in M files, in order to be used in the test stage of the respective networks. We have applied this model as a classifier, in a cascade having the following processing stages: the application of a pattern to the input of each of the M networks; computing the output of the respective network and then taking the maximum of those M outputs. The results of computer simulation will be given.

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DOI: 10.5151/meceng-wccm2012-19128

Referências bibliográficas

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Como citar:

Iatan, I. F.; "A CONCURRENT FUZZY NEURAL NETWORK APPROACH FOR A FUZZY GAUSSIAN NEURAL NETWORK", p-3018-3025. In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 23580828, DOI 10.5151/meceng-wccm2012-19128