CONSTRUCTION OF SWITCH NONLINEAR DYNAMIC SYSTEM USING ADAPTIVE NEURAL NETWORK TECHNOLOGY

Abstract

In order to improve the intelligent tracking efficiency of switched nonlinear dynamic system, first, the optimized backpropagation neural network (BPNN) is proposed and the switched nonlinear dynamic system is constructed. Then, the optimized BPNN is extended and the adaptive control is studied. Finally, the extended neural network system related to the error is formed, and then the simulation analysis is carried out. The results show that under different reference signal conditions, the output peak values of the system are 1.7 and 1.5 at 2[Formula: see text]s, respectively, while the response peak value of the reference signal is 1. However, after 4[Formula: see text]s, the peak values of the two signals are very close; under different reference signals, the scaling factor decreases with the increase of time. After 6[Formula: see text]s, the value of scaling factor is 0. Under the condition of reference signal 1, the estimated values of parameters decrease with the increase of time, and tend to 0 after 6[Formula: see text]s. However, under the condition of reference signal 2, the estimated values of parameters in the first 6[Formula: see text]s increase with the increase of time; the peak values of the state variable [Formula: see text] and the adaptive parameters in the system are in a stable state, while the peak values of the system controller and the system tracking error gradually decrease with the increase of time, and finally tend to a stable value. It suggests that the technique proposed in this work can guarantee the signal in switched nonlinear dynamic system to be bounded.