基于动态BP算法的非线性滞后系统辨识

Nonlinear time delay systems identification based on dynamic BP algorithm

非线性动态系统的建模一直是控制领域的重要问题之一.针对这一问题,特别是包含滞后环节的非线性系统建模问题,提出了一种引入自适应延迟的动态BP(back propagation)学习算法.该算法在传统多层感知机神经网络结构基础上,在网络的第1隐层和输出层分别引入可调节的自适应延迟参数,通过误差梯度对其进行修正,实现了对延迟参数的辨识.仿真结果表明,所提出的方法能够有效实现对非线性滞后系统的辨识,并能够对系统的延迟时间进行准确估计.

The modeling of nonlinear dynamic system is an important problem in the domain of automatic control.For the problem,especially for the nonlinear time delay system,a novel dynamic BP（back propagation）neural network algorithm with adaptive time delay parameters is proposed.Based on the traditional multilayer perceptron neural network structure,the adaptive time delay parameters are employed in the first hidden layer and output layer.The gradient descent method helps to realize the parameter adjusting and time delay estimation.The simulation results show that the proposed method can not only identify the nonlinear system effectively,but also estimate time delay exactly.