复数FIR DF设计的神经网络优化方法

A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS

本文基于人工神经网络(ANN)能量函数优化理论,提出了一种FIR数字滤波器(DF)神经网络优化设计(NNO)方法的理论框架。该理论将实数与复数FIR DF设计工作统一起来。表征设计质量的加权均方误差被当作ANN能量函数,以此导出FIR-NNO的Lyapunov方程。文中说明了算法实现的基本原则,并给出了两个实数线性相位和一个复数非线性相位FIR DF设计实例。通过与其它几种方法的比较证明了该方法的有效性。

A novel complex FIR filter design approach based on Neural Network Optimiza-tion(NNO) technique is proposed in this paper. To demonstrate the feasibility of the NNO design approach, the weight least mean square criterion between the disired frequency response and the designed filter response is defined as the Lyapunov energy function of a continuous Hopfield network, and the network state equations are drived. The implementation of the NNO approach is described together with some design guidelines. A few design examples are given and the advantages of NNO approach over conventional methods are illustrated.