基于泛函连接网络和差分进化算法的后非线性混叠信号盲分离方法

把后非线性混叠信号盲分离的分离系统用泛函连接网络来建模，对分离系统的输出应用高阶统计量独立性准则作为测度，然后利用差分进化算法对泛函连接网络的权值进行学习，从而获得了一种后非线性混叠信号盲分离算法。由于泛函连接网络是一种单层神经网络，具有学习参数少、收敛速度快和非线性逼近能力强的特点；而差分进化算法控制参数少、易于选择、具有全局寻优能力和快速的收敛特性；因而与其它的后非线性混叠信号盲分离方法相比，该文提出的分离算法具有计算简单、收敛速度快、较高的精度和稳定性好的特点。仿真结果显示了这种方法是可行和有效的。

Blind Source Separation of Nonlinear Mixtures Based on Functional Link Artificial Neural Networks and Differential Evolution Algorithm

In this paper, a post nonlinear blind sources separation method is proposed. The demixing system of the post nonlinear mixtures is modeled using a functional link artificial neural network whose weights can be determined under the criterion of independence of its outputs. A criterion of independence based on higher order statistics is used to measure the statistical dependence of the outputs of the demixing system, and the differential evolution algorithm is utilized to minimize the criterion. The proposed method takes advantage of less learning parameters, high learning convergence rate of parameters, nonlinear approximation capability of the functional link artificial neural network, and few easily chosen control parameters, global optimization capability of the differential evolution algorithm. Compared to conventional post nonlinear blind sources separation approaches, the proposed approach for post-nonlinear blind source separation is characterized by less computational load, high convergence rate, high accuracy and robustness. Simulation results show that the proposed approach is capable of separating independent sources from their post-nonlinear mixtures.