盲信号分离的细胞神经网络方法

Blind Signal Separation Using Cellular Neural Networks

本文用双层的细胞神经网络（ＣＮＮ）进行盲信号分离。提出ＣＮＮ的拓扑结构和内部参数。ＣＮＮ的第一层功能是一只自适应渐近收敛于平衡点的滤波器。一个平稳随机的模型，是第一层收敛的细胞，用于探测的必要条件，也是对标准的平衡状态解的主要条件。第二层的功能是一个信号分离器。仿真显示，ＣＮＮ盲信号分离器具有良好的鲁棒性，而且工作平稳。

In this paper a two-layer cellular neural network(CNN) is used to separate blind signals. The topological structures of the CNN and the inner parameters are presented. The first CNN layer functions as an adaptive filter which converges asymptotically to an equilibrium point in the mean. A stochastic stability model is used to find conditions under which cells in the first layer converge. Conditions leading to correct equilibrium solutions are also presented using this model. The second CNN layer functions as a signal separator. Simulations show that the CNN blind signal separator has strong robustness and works even better than the theory predicts.