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Neural Computing and Applications

Erschienen in:

01.04.2010 | Original Article

An adaptive wavelet network for function learning

verfasst von: Yusuf Oysal, Sevcan Yilmaz

Erschienen in: Neural Computing and Applications | Ausgabe 3/2010

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Abstract

In this article, a wavelet neural network (WNN) model is proposed for approximating arbitrary nonlinear functions. Our WNN model structure comes from the idea of adaptive neuro-fuzzy inference system (ANFIS) which is used for obtaining fuzzy rule base from the input–output data of an unknown function. The WNN model which is called in this study as adaptive wavelet network (AWN) consists of wavelet scaling functions in its processing units whereas in an ANFIS, mostly Gaussian-type membership functions are used for a function approximation. We present to train an AWN by a hybrid-learning method containing least square estimation (LSE) with gradient-based optimization algorithm to obtain the optimal translation and dilation parameters of our AWN for model accuracy. Simulation examples are also given to illustrate the effectiveness of the method.

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Titel: An adaptive wavelet network for function learning
verfasst von: Yusuf Oysal
Sevcan Yilmaz
Publikationsdatum: 01.04.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0297-4

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