A resource-allocating network for function interpolation
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Nonlinear adaptive control using neural networks and multiple models
Automatica (Journal of IFAC)
A discrete-time system adaptive control using multiple models and RBF neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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A novel multiple models adaptive control method is proposed to improve the dynamic performance of complex nonlinear systems under different operating modes. Multiple linearized models are established at each equilibrium point of the system. Each local linearized model is valid within a neighborhood of the point, and then an improved RBF algorithm is applied to compensate for modeling error. Simulation results are presented to demonstrate the validity of the proposed method.