自适应CRBF非线性滤波器及其改进学习算法

Adaptive CRBF Nonlinear Filter and its Improved Learning Algorithm

传统的随机梯度算法由于采用基于二阶统计量的平方误差代价函数,因此含有的信息量较少,难以实现更高的精度。针对此问题,以基于高阶统计量的指数平方误差作为代价函数,结合基于两层RBF网络凸组合的非线性自适应滤波器,提出了最小指数平方误差自适应学习算法。非线性系统辨识和非线性信道均衡的实验仿真结果表明,该改进算法的收敛性能明显优于传统的随机梯度算法。

The traditional stochastic gradient algorithm uses squared error cost function based on second order statis- tics. It is difficult to achieve higher precision because it contains less information. To solve the problem, a new minimum exponential squared error adaptive learning algorithm was put forward. It uses exponential squared error cost function based on high order statistics, and combines the nonlinear adaptive filter based on convex combination of two RBF net- works. The simulation experimental results of nonlinear system identification and nonlinear channel equalization show that the convergence performance of the improved algorithm is superior to the traditional stochastic gradient algorithm.