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Research Article

Comparision of Neural Algorithms for Funchtion Approximation

Lale Ozyilmaz , Tulay Yildirim and Kevser Koklu
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In this work, various neural network algorithms have been compared for function approximation problems. Multilayer Perceptron (MLP) structure with standard back propagation, MLP with fast back propagation (adaptive learning and momentum term added), MLP with Levenberg-Marquardt learning algorithms, Radial Basis Function (RBF) network structure trained by OLS algorithm and Conic Section Function Neural Network (CSFNN) with adaptive learning have been investigated for various functions. Results showed that the neural algorithms can be used for functional estimation as an alternative to classical methods.

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  How to cite this article:

Lale Ozyilmaz , Tulay Yildirim and Kevser Koklu , 2002. Comparision of Neural Algorithms for Funchtion Approximation. Journal of Applied Sciences, 2: 288-294.

DOI: 10.3923/jas.2002.288.294


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