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针对非线性Wiener模型的参数辨识问题,提出了一种基于Sigmoid函数及自适应算子改进差分进化(improved differential evolution algorithm with Sigmoid function and adaptive mutation operator,SADE)算法的参数辨识方法。利用Sigmoid函数及自适应变异算子改进了基本差分进化算法的变异操作部分,改进的方法能够有效地克服基本差分进化算法的过早收敛和不稳定性等缺点。将该改进差分进化算法应用于对非线性Wiener模型的参数辨识问题,达到了较高的辨识精度。在仿真试验中,与其它已有方法进行比较,仿真结果说明了所给的参数辨识方法是合理和有效的。
Aiming at the problem of parameter identification of nonlinear Wiener model, a parameter identification method based on Sigmoid function and adaptive differential evolution (SADA) algorithm is proposed. The Sigmoid function and adaptive mutation operator are used to improve the mutation operation of the basic differential evolution algorithm. The improved method can effectively overcome the shortcomings of premature convergence and instability of the basic differential evolution algorithm. The improved differential evolution algorithm is applied to the parameter identification of nonlinear Wiener model, which achieves high recognition accuracy. In the simulation experiment, compared with other existing methods, the simulation results show that the given parameter identification method is reasonable and effective.