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- W1978985436 abstract "Dealing with multidimensional problems has been the “bottle-neck” 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." @default.
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- W1978985436 date "1998-12-01" @default.
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- W1978985436 title "Multidimensional non-orthogonal wavelet-sigmoid basis function neural network for dynamic process fault diagnosis" @default.
- W1978985436 doi "https://doi.org/10.1016/s0098-1354(98)00258-0" @default.
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