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Logical radial basis function networks a hybrid intelligent model for function approximation
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DOI：10.1016/S0965-9978(98)00124-0.png)
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摘要
En 中文
The aim of this article is to introduce a new approach for fuzzy neural network models which can be used effectively in function approximation problems. The proposed model is introduced as an adaptive two-level fuzzy inference system. The architecture of the model is basically a two-layer network of new types of fuzzy-neurons which act as fuzzy IF-THEN rules. The model can be considered as a logical version of the Radial Basis Function networks (RBF). Genetic Algorithms have been adopted as the learning mechanism of the proposed model. Simulations show both highly nonlinear mapping and reasoning capabilities together with simpler structure and better performance when compared with classical neural networks. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
fuzzy inference system
neural network
genetic algorithms
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