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Hopfield neural networks are the recurrent artificial neural networks. A novel method using Gaussian-Hopfield neural network (GHNN) to identify the discrete-time nonlinear systems is proposed. And then, the genetic algorithm is used to search for global optimization, which can improve the speed of searching for the global optimal parameters for the Gaussian basis functions (GBFs). In the end, simulation experiments are described in detail. The simulation results demonstrated that the proposed method can be successfully used to identify discrete-time nonlinear systems with good performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]