Identification of nonlinear dynamics using a general-spatio-temporal network

DOI10.1016/0895-7177(94)00195-TzbMath0818.68125MaRDI QIDQ1346678

A. G. Parlos, Amir F. Atiya

Publication date: 10 April 1995

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

zbMATH Keywords

spatio-temporal neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Dynamics and architecture for neural computation

Cites Work

• Adaptation and tracking in system identification - a survey
• Adjoint-operators and non-adiabatic learning algorithms in neural networks
• Dynamics and architecture for neural computation
• Identification of systems containing linear dynamic and static nonlinear elements
• Multilayer feedforward networks are universal approximators
• Structure identification of nonlinear dynamic systems - A survey on input/output approaches
• Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
• Non-linear system identification using neural networks
• Practical identification of NARMAX models using radial basis functions
• Structure detection and model validity tests in the identification of nonlinear systems
• Representations of non-linear systems: the NARMAX model
• Identification of MIMO non-linear systems using a forward-regression orthogonal estimator
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Approximation by superpositions of a sigmoidal function

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