Dynamics and architecture for neural computation
Journal of Complexity - Special Issue on Neural Computation
Multilayer feedforward networks are universal approximators
Neural Networks
Adaptation and tracking in system identification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Back propagation in non-feedforward networks
Neural computing architectures
Time dependent adaptive neural networks
Advances in neural information processing systems 2
Adjoint-functions and temporal learning algorithms in neural networks
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Identification of systems containing linear dynamic and static nonlinear elements
Automatica (Journal of IFAC)
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Comparison of four neural net learning methods for dynamic system identification
IEEE Transactions on Neural Networks
Predicting the News of Tomorrow Using Patterns in Web Search Queries
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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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.