谐波级数表示与周期时变参数模型间关系

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.

Relation between harmonic series representation and periodically time-varying parametric model

A novel method to obtain the periodically time-varying parametric model from the harmonic series representation (HSR) of a cyclostationary random process is proposed. Firstly, the joint stationary random process in the HSR is represented by moving average (MA) parameter model and then a periodically time-varying parametric model is modeled by using the periodicity of harmonic signal. Lastly, the consistency between the two models is illustrated by the simulations. The theoretical analysis and simulation results show that periodically time-varying parameter model can be obtained from the HSR of a cyclostationary process in certain accuracy.