混沌通信系统中非线性信道的自适应组合神经网络均衡

针对混沌通信系统的非线性信道干扰问题，基于混沌信号重构理论和函数型连接神经网络理论，提出了一种横向滤波器与函数型连接神经网络组合(combination of transversal filter and functional link neural network，CFFLNN)的自适应非线性信道均衡器，并给出基于低复杂度归一化最小均方(NLMS)的自适应算法，并对该均衡器的稳定性以及收敛条件进行了分析.该非线性自适应均衡器充分利用了横向滤波器的快速收敛，以及函数型连接神经网络通过增大输入空间提高非线性逼近能力的特点，进一步提高均衡器的收敛速度和降低稳态误差.仿真研究表明：所提出的非线性自适应均衡器能够有效地消除线性和非线性信道干扰，均衡器输出信号能反映出混沌信号的特性，具有良好的抗干扰性能；且该均衡器的结构简单，收敛稳定性较好，易于工程实现. 关键词： 非线性信道 自适应均衡器 混沌吸引子 神经网络

Adaptive nonlinear channel equalization based on combination neural network for chaos-based communication systems

For nonlinear channel distortions of chaos-based communication systems，based on the analysis of the theory of chaotic signal reconstruction and the characteristics of transversal filter and functional link neural network (FLNN)，a novel nonlinear adaptive equalizer with the architecture of combination of transversal filter and functional link neural network (CFFLNN) is proposed in this paper. The novel nonlinear equalizer fully utilizes faster convergence characteristics of transversal filter and the nonlinear approximation capability of FLNN by function expansion due to enhanced input space. Furthermore，the performance of the novel nonlinear adaptive equalizer is also improved. Finally，the proposed equalizer is designed and its adaptive algorithm is deduced by the low complexity normalized least mean square (NLMS) method. And an analysis of stability and convergence for the derived algorithm is provided. To illustrate the analysis，results obtained from the computer simulation are also provided for both linear and nonlinear channels in chaos-based communication system.