Convergence Analysis of Online Decoupling Based on Neural Network

摘要：

Aimed at a novel online decoupling algorithm of the neural network, which makes the cross-correlation function as the target function and the weights are trained by the hybrid genetic algorithms based on real (floating)-coded, its convergence is analyzed. Firstly, the uniform random step sequence is selected to excite adequately the MIMO system. Secondly, it is discussed that cross-correlation function has some effect on the convergence of algorithm. Finally, the convergence of the Hooke-Jeeves pattern search and FGA is discussed. The theoretical analysis and simulation results indicate that the algorithm is convergent and efficient.

作者: Xinli LI Guotian YANG Yan BAI

作者单位: North China Electric Power University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）