IDENTIFICATION OF NONLINEAR DYNAMICS USING A GENERAL SPATIOTEMPORAL NETWORK

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.