Multidimensional non-orthogonal wavelet-sigmoid basis function neural network for dynamic process fault diagnosis |
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Affiliation: | 1. Department of Chemical Engineering, Tsinghua University, Beijing, China, 100084;2. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing, China, 100084;1. Department of Chemical Engineering, Tsinghua University, Beijing, China, 100084;2. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing, China, 100084 |
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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. |
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