Wavelet Based Nonlinear Identification
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The approximation of general continuous functions by nonlinear networks has been widely applied to system modelling and identification. Such approximation methods are particularly useful in the black-box identification of nonlinear systems where very little a priori knowledge is available. For example, neural networks have been established as a general approximation tool for fitting nonlinear models from input output data on the basis of the universal approximation property of such networks. There has also been considerable recent interest in identification of general nonlinear systems based on radial basis networks (Poggio and Girosi, 1990a,b), fuzzy sets and rules (Zadeh, 1994), neural-fuzzy networks (Brown and Harris, 1994; Wang et al., 1995) and hining hyperplanes (Breiman, 1993).
KeywordsModelling Error Scaling Function Wavelet Network Reconstruction Sequence Minimum Response Time
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