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• W1971406372 endingPage "71" @default.
• W1971406372 startingPage "53" @default.
• W1971406372 abstract "The so-called spatio-temporal neural network is considered. This is a neural network where the conventional weight multiplication operation is replaced by a linear filtering operation. General learning algorithms are derived for such a network, both in the discrete-time and in the continuous-time domains. The problem of deterministic nonlinear system identification is considered as an application of spatio-temporal neural networks. Nonlinear system identification is one of the challenging problems in the field of dynamic systems, with limited successful results using conventional methods. Neural network approaches have so far been encouraging, but further exploration is needed. The capabilities of the derived algorithms and of the considered architectures to effectively identify deterministic nonlinear systems is demonstrated through examples." @default.
• W1971406372 created "2016-06-24" @default.
• W1971406372 creator A5013978386 @default.
• W1971406372 creator A5017676848 @default.
• W1971406372 date "1995-01-01" @default.
• W1971406372 modified "2023-09-23" @default.
• W1971406372 title "Identification of nonlinear dynamics using a general spatio-temporal network" @default.
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• W1971406372 doi "https://doi.org/10.1016/0895-7177(94)00195-t" @default.
• W1971406372 hasPublicationYear "1995" @default.
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