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Identification of Infinite Dimensional Systems Via Adaptive Wavelet Neural Networks

dc.contributor.authorZhuang, Y.en_US
dc.contributor.authorBaras, John S.en_US
dc.description.abstractWe consider identification of distributed systems via adaptive wavelet neural networks (AWNNs). We take advantage of the multiresolution property of wavelet systems and the computational structure of neural networks to approximate the unknown plant successively. A systematic approach is developed in this paper to find the optimal discrete orthonormal wavelet basis with compact support for spanning the subspaces employed for system identification. We then apply backpropagation algorithm to train the network with supervision to emulate the unknown system. This work is applicable to signal representation and compression under the optimal orthonormal wavelet basis in addition to autoregressive system identification and modeling. We anticipate that this work be intuitive for practical applications in the areas of controls and signal processing.en_US
dc.format.extent907890 bytes
dc.relation.ispartofseriesISR; TR 1993-64en_US
dc.subjectneural systemsen_US
dc.subjectsignal processingen_US
dc.subjectneural networksen_US
dc.subjectsystem identificationen_US
dc.subjectSystems Integrationen_US
dc.titleIdentification of Infinite Dimensional Systems Via Adaptive Wavelet Neural Networksen_US
dc.typeTechnical Reporten_US

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