System identification: theory for the user
System identification: theory for the user
Universal approximation using radial-basis-function networks
Neural Computation
Twenty-one ML estimators for model selection
Automatica (Journal of IFAC)
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Backward Elimination Methods for Associative Memory Network Pruning
International Journal of Hybrid Intelligent Systems
Regularization in the selection of radial basis function centers
Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust nonlinear model identification methods using forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Adaptive motion control using neural network approximations
Automatica (Journal of IFAC)
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
On the efficiency of the orthogonal least squares training method for radial basis function networks
IEEE Transactions on Neural Networks
A hybrid linear/nonlinear training algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Selecting radial basis function network centers with recursive orthogonal least squares training
IEEE Transactions on Neural Networks
Two highly efficient second-order algorithms for training feedforward networks
IEEE Transactions on Neural Networks
Effects of moving the center's in an RBF network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks
Comments on “Pruning Error Minimization in Least Squares Support Vector Machines”
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A fast multi-output RBF neural network construction method
Neurocomputing
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The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.