System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Nonlinear system identification using a Gabor/Hopfield network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamical systems using the self-organizing map
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The neural network can be used to identify unknown systems. A novel method based on delta-learning rules to identify the nonlinear system is proposed. First, a single-input-single-output (SISO) discrete-time nonlinear system is introduced, and Gaussian basis functions are used to represent the nonlinear functions of this system. Then the adjustable parameters of Gaussian basis functions are optimized by using delta-learning rules. In the end, simulation results are illustrated to demonstrate the effectiveness of the proposed method.