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| 基于聚类的超闭球CMAC神经网络改进算法 | |
| 引用本文： | 李慧,段培永,张庆范.基于聚类的超闭球CMAC神经网络改进算法[J].吉林大学学报(工学版),2012,42(1):170-175. |
| 作者姓名： | 李慧 段培永 张庆范 |
| 作者单位： | 1. 山东建筑大学可再生能源建筑利用技术省部共建教育部重点实验室,济南250101／山东建筑大学山东省建筑节能技术重点实验室,济南250101 2. 山东建筑大学山东省智能建筑技术重点实验室,济南,250101 3. 山东大学控制科学与工程学院,济南,250061 |
| 基金项目： | 国家自然科学基金项目，山东省自然科学基金项目，山东省科技攻关项目 |
| 摘 要： | 针对CMAC神经网络的网络节点随输入维数的增大呈几何级数增加的问题,提出了基于模糊聚类的超闭球CMAC神经网络改进算法。该算法通过对输入数据进行模糊聚类确定网络节点数和节点值,并根据输入输出数据通过模糊推理优化算法计算神经网络初始权值。与原算法比较,该算法可有效降低神经网络节点数,提高系统的学习精度。对一个多步时延的非线性系统的辨识仿真结果表明了该算法的可行性与有效性。 |
| 关 键 词： | 人工智能 CMAC神经网络 聚类 模糊推理 学习 |
Improved hyperball CMAC neural network algorithm based on clustering |
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| LI Hui,DUAN Pei-yong,ZHANG Qing-fan.Improved hyperball CMAC neural network algorithm based on clustering[J].Journal of Jilin University:Eng and Technol Ed,2012,42(1):170-175. | |
| Authors: | LI Hui DUAN Pei-yong ZHANG Qing-fan |
| Affiliation: | 1.Key Laboratory of Renewable Energy Utilization Technologies in Buildings of Ministry of Education,Shandong Jianzhu University,Ji’nan 250101,China;2.Shandong Key Laboratory of Building Energy-saving Technologies,Shandong Jianzhu University,Ji’nan 250101,China;3.Shandong Key Laboratory of Intelligent Buildings Technologies,Shandong Jianzhu University,Ji’nan 250101,China;4.School of Control Science and Engineering,Shandong University,Ji’nan 250061,China) |
| Abstract: | The number of nodes of the cerebelar model articulation controller(CMAC) neural network increases exponentially with the input dimensions.To overcome such drawback,an improved hyperball CAMC neural network algorithm based on clustering was proposed.A fuzzy clustering algorithm was adopted to determine the node number and node values of the neural network by clustering the input data.A fuzzy inference optimization algorithm was proposed to calculate the initial weight value of the neural network based on input-output data.Compared with the original hyperball CAMC,the improved algorithm can effectively reduce the neural network nodes and improve the learning accuracy.The multi-step time-delay nonlinear dynamic system simulation results demonstrate the feasibility and superiority of the proposed algorithm. |
| Keywords: | artificial intelligence CMAC neural network clustering fuzzy inference learning |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
