广义回归神经网络的改进及在预测控制中的应用  

The Generalized Regression Neural Network and Its Application to Predictive Control

作　　者：王少福[1] 张金磊[2] 赵仕俊[1] 杨卫东[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083 [2]中国石油大学计算机与通信工程学院,山东东营257061

出　　处：《微电子学与计算机》2009年第6期32-35,共4页Microelectronics & Computer

摘　　要：针对广义回归网络的模式层单元数目与样本数量成正比的问题,提出了基于相似度衡量的模糊均值聚类的样本精简方法.针对广义回归网络在时变环境下难以确定平滑因子,自适应能力弱的缺点,提出了一种基于贡献率的选择优化方案.仿真结果表明,改进后的GRNN有较快的处理速度和较强的自适应能力,能够在实际应用中很好地辨识较为复杂的非线性时变系统.For the defect that pattern layer neurons are proportional to the number of the training samples, the method of fuzzy means clustering based on similarity index to decrease samples is proposed. For the defect that it is hard to determine the smoothing parameter in time-varying conditions, the adaptive optimizing strategy based on contributing ratio is proposed. The simulation results show that the improved GRNN has fast solving speed and good adaptability. It can approach complex nonlinear time-varying systems well.

关 键 词：广义回归神经网络 模糊均值聚类 平滑因子 系统辨识 预测控制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]