改进的互信息最小化非线性盲源分离算法

提出了一种改进的互信息量最小化非线性盲源分离算法,改善了优化算法在串音误差方面大等的不足。该方法利用自然梯度优化算法来优化目标函数,避免了对矩阵的求逆计算,减少了计算时间。此外,在网络参数优化的过程中引入了扰动信号,提高了非线性盲源分离算法的寻优能力。实验结果表明改进的非线性盲源分离算法是有效的,而且相对传统的非线性盲源分离方法具有较小的误差。

Improved Mutual Information Minimization AlgorithmFor Nonlinear Blind Source Separation

This paper presents an improved mutual information minimization algorithm for nonlinear blind source separation,perfects the optimization algorithm in terms of the crosstalk error(ECT) big,etc.The method uses the natural gradient optimization algorithm to optimize the objective function,this can avoid the matrix inversion calculation and reduce the computing time.Focus is on the introduction of disturbance signals in the network to optimize the process parameters.It prevents stagnation in the late algorithm,improves the ability of the optimization of nonlinear blind source separation algorithm.Experimental results show that the proposed nonlinear blind source separation optimization algorithm is a good algorithm,obtains effective,stable separation results.The experiment proves that the new optimization algorithm is superior to the traditional optimization algorithm.