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[1]黄金杰,夏静,满春涛,等.一种参数优化旋转广义回归神经网络模型 [J].电机与控制学报,2009,(03):442-447.
 HUANG Jin-jie,XIA Jing,MAN Chun-tao,et al.Parameter-optimized rotated general regression neural network model[J].,2009,(03):442-447.
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一种参数优化旋转广义回归神经网络模型
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2009年03
页码:
442-447
栏目:
出版日期:
2009-05-15

文章信息/Info

Title:
Parameter-optimized rotated general regression neural network model
作者:
黄金杰; 夏静; 满春涛; 王松涛
哈尔滨理工大学自动化学院; 哈尔滨理工大学测控技术与通信工程学院
Author(s):
HUANG Jin-jie; XIA Jing; MAN Chun-tao; WANG Song-tao
关键词:
广义回归神经网络 粒子群优化 坐标旋转 参数优化
Keywords:
general regression neural network particle swarm optimization coordinate rotation parameter optimization
分类号:
TP183
DOI:
-
文献标志码:
A
摘要:
针对传统广义回归神经网络的模型结构与数据分布失配问题和模型参数难以确定问题,提出了一种参数优化旋转广义回归神经网络模型的设计方法。在传统广义回归神经网络模型的基础上,通过坐标旋转,增加了一个模型结构参数,并采用粒子群算法对旋转广义回归神经网络的模型参数寻找最优值,从而改进了广义回归神经网络模型精确度。两个工业实例的实验结果表明该方法的有效性。
Abstract:
To resolve the problem of the mismatching of model structure and data distribution as well as the problem of determining model parameters difficultly in the traditional general regression neural network(GRNN),a scheme is proposed to design a parameter-optimized rotated general regression neural network.Through the coordinate rotation,an additional parameter of model structure is introduced to the traditional general regression neural network.Moreover,the particle swarm optimization algorithm is adopted to find the best values of parameters of the rotated GRNN;hence the model precision is improved.The experimental results of two industrial applications have shown the effectiveness of the method.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金(60575036);; 哈尔滨市科技创新人才研究专项资金项目(2007RFXXG023);; 哈尔滨理工大学优秀拔尖创新人才培养基金(20080103)
更新日期/Last Update: 2009-07-09