| 基于AQPSO的RBF神经网络自组织学习 |
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| 引用本文： | 杨刚,王乐,戴丽珍,杨辉,陆荣秀.基于AQPSO的RBF神经网络自组织学习[J].控制与决策,2018,33(9):1631-1636. |
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| 作者姓名： | 杨刚 王乐 戴丽珍 杨辉 陆荣秀 |
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| 作者单位： | 华东交通大学电气与自动化工程学院，南昌330013;江西省先进控制与优化重点实验室，南昌330013,华东交通大学电气与自动化工程学院，南昌330013;江西省先进控制与优化重点实验室，南昌330013,华东交通大学电气与自动化工程学院，南昌330013;江西省先进控制与优化重点实验室，南昌330013,华东交通大学电气与自动化工程学院，南昌330013;江西省先进控制与优化重点实验室，南昌330013,华东交通大学电气与自动化工程学院，南昌330013;江西省先进控制与优化重点实验室，南昌330013 |
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| 基金项目： | 国家自然科学基金项目(61673172,61663012,61364013)；江西省交通运输厅科技项目(2014X0015)；江西省教育厅科技项目(GJJ150490)；江西省科技厅青年科学基金项目(20161BAB212054). |
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| 摘 要： | 针对径向基函数(RBF)神经网络的结构设计及参数优化问题,提出一种自适应量子粒子群优化(AQPSO)算法.将RBF神经网络的网络规模及参数映射到粒子的空间位置,定义权值平均最优位置,从而对量子粒子群优化(QPSO)中$L_{i,j
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| 关 键 词： | RBF神经网络 自适应量子粒子群优化 自组织学习 |
AQPSO-based self-organization learning of RBF neural network |
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| YANG Gang,WANG Le,DAI Li-zhen,YANG Hui and LU Rong-xiu.AQPSO-based self-organization learning of RBF neural network[J].Control and Decision,2018,33(9):1631-1636. |
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| Authors: | YANG Gang WANG Le DAI Li-zhen YANG Hui and LU Rong-xiu |
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| Affiliation: | School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013,China,School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013,China,School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013,China,School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013,China and School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013,China |
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| Abstract: | Aiming at the structural design and parameter optimization problems of radial basis function(RBF) neural network, an adaptive quantum-behaved particle swarm optimization(AQPSO) algorithm is proposed. In order to realize the self-organization learning of RBF nerual network and improve the performance, the network size and parameters of RBF neural network are mapped to the spatial position of the particles firstly, and then the weight mean of best particle positions is defined to evaluate $L_{i,j |
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