基于非线性Volterra信道的复数神经多项式盲均衡算法

摘要

Aiming at the low convergence rate and high mean square error of traditional constant modulus algorithm(CMA) and too many parameters and high complexity of traditional neural network ,a complex neural polynomial blind equalization algorithm based on nonlinear Volterra channel is studied . In the studied algorithm ,the complex-valued neural polynomial with a single layer neural network and nonlinear processor has very simple structure and low complexity .And the fuzzy rule controller based on fuzzy neu-ral network (FNN) can effectively control the step-size of scale factor .The simulation results show that the proposed algorithm not only has simple structure ,low complexity ,fast convergence speed and small steady-state error , but also can solve the contradiction between convergence speed and mean square error .%针对传统常模算法收敛速度慢、均方误差大以及传统神经网络参数多、复杂度高的问题,提出了基于非线性Volterra信道的复数神经多项式盲均衡算法(Fuzzy neural network-complex valued neural polynomial-constant modulus algorithm,FNN-CNP-CMA).该算法包含单层神经网络和非线性处理器的复数神经多项式,模块结构简单、复杂度低.由模糊神经网络(Fuzzy neural network,FNN)设计的模糊规则控制器能有效提高步长的控制精度.仿真实验结果表明,该算法系统结构简单、复杂度低、收敛速度快且稳态误差小,较好地解决了收敛速度与均方误差之间存在的矛盾.