System identification: theory for the user
System identification: theory for the user
Advances in neural information processing systems 2
Model selection in neural networks
Neural Networks
Pruning from adaptive regularization
Neural Computation
Identification and control of dynamical systems using 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
IEEE Transactions on Neural Networks
Pruning recurrent neural networks for improved generalization performance
IEEE Transactions on Neural Networks
Combined input variable selection and model complexity control for nonlinear regression
Pattern Recognition Letters
A fast multi-output RBF neural network construction method
Neurocomputing
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Neural input selection is an important stage in neural network configuration. For neural modeling and control of nonlinear dynamic systems, the inputs to the neural networks may include any system variable of interest with various time lags. To choose a set of significant inputs is a combinational problem, and the selection procedure can be very time consuming. In this paper, a model-based neural input selection method is proposed. Essentially, the neural input selection is transformed into the problem of identifying the significant terms for a linear-in-the-parameters model. A fast method is then proposed to identify significant nonlinear terms or functions, from which the neural inputs are grouped and selected. Both theoretic analysis and simulation examples demonstrate the effectiveness and efficiency of the proposed model-based approach.