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期刊论文
Multidimensional non-orthogonal wavelet-sigmoid basis function neural network for dynamic process fault diagnosis
Computers and Chemical Engineering 23(1998)83-92,-0001,():
Dealing with multidimensional problems has been the ÔÔbottle-neckÕÕ for implementing wavenets to process systems engineering. To tackle this problem, a novel multidimensional wavelet (MW) is presented with its rigorously proven approximation theorems. Taking the new wavelet function as the activation function in its hidden units, a new type of wavenet called multidimensional non-orthogonal non-product wavelet-sigmoid basis function neural network (WSBFN) model is proposed for dynamic fault diagnosis. Based on the heuristic learning rules presented by authors, a new set of heuristic learning rules is presented for determining the topology of WSBFNs. The application of the proposed WSBFN is illustrated in detail with a dynamic hydrocracking process. (1998 Elsevier Science Ltd. All rights reserved.
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