Multilayer feedforward networks are universal approximators
Neural Networks
Discrete Orthonormal Sequences
Journal of the ACM (JACM)
Fast learning in networks of locally-tuned processing units
Neural Computation
IEEE Transactions on Information Theory
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Bounds on the number of hidden neurons in multilayer perceptrons
IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks
IEEE Transactions on Neural Networks
A simple method to derive bounds on the size and to train multilayer neural networks
IEEE Transactions on Neural Networks
Identification structures using rational wavelets: examples of application
Applied Numerical Mathematics - Special issue on applied and computational mathematics: Selected papers of the fourth PanAmerican workshop
Rational wavelets in Wiener-like modeling
Mathematical and Computer Modelling: An International Journal
Nonlinear uncertainty model of a magnetic suspension system
Mathematical and Computer Modelling: An International Journal
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In this paper, a methodology to obtain approximate models from input-output data for nonlinear, causal, time invariant discrete systems having a certain type of continuity condition called fading memory is presented. The region or domain of the input space, where the model can be applicable, is studied, as well as the importance of this study in applications as data processing and the qualification of the model quality. The structure is synthesized using a finite set of discrete Kautz systems, followed by a single hidden layer perceptron. The number of the Kautz systems is evaluated by Lipschitz quotients, while the number of hidden neurons is bounded using a pruning technique. Examples illustrating the proposed methodology are presented.