IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
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A novel multi-input, multi-output generalized radial basis function (RBF) neural networks for nonlinear system modelling is presented in the paper, which uses extend Kalman filter to sequentially update both the output weights and the centers of the network. Simultaneously, such RBF models employ radial basis functions whose form is determined by admissible exponential generator functions. To test the validity of the proposed method, this paper demonstrates that generalized RBF neural networks with the extended Kalman filter can be used effectively for the identification and modelling of nonlinear dynamical systems. Simulation results reveal that the new generalized RBF networks guarantee faster learning and very satisfactory function approximation capability in modeling nonlinear dynamic systems.