针对目前Volterra频域核辨识方法复杂、精度不高等问题, 提出一种基于神经网络的Volterra频域核辨识方法。首先选择多组频率基准确测量各阶Volterra频域核的幅值, 利用BP神经网络可以任意逼近非线性函数的特点, 针对不同阶Volterra频域核设计不同的神经网络模型, 进行分阶辨识, 最后通过一个非线性电路进行仿真验证。仿真结果表明, 该方法可直接辨识频率范围内任意频率对应的Volterra频域核, 过程简单、准确度高, 易于工程实现。

In order to solve the problem of high complexity and low accuracy of the current method for Volterra frequency-domain kernel identification, a method for Volterra frequency-domain kernel identification based on neural network is proposed. Firstly, the amplitude of each Volterra frequency-domain kernel is accurately measured after choosing multiple frequency components. Then, we use the characteristics of BP neural network that it can approximate nonlinear functions to design different models for different-order Volterra frequency-domain kernels, so as to identify each kernel. Finally, a nonlinear circuit is adopted for simulation. The results show that this method can directly identify all the Volterra frequency-domain kernels in the frequency range, and the process is simple with high accuracy, which is suitable for engineering realization.