| 基于GEP优化的RBF神经网络算法 |
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| 引用本文： | 陶俊剑,元昌安,蔡宏果.基于GEP优化的RBF神经网络算法[J].小型微型计算机系统,2010,31(5). |
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| 作者姓名： | 陶俊剑 元昌安 蔡宏果 |
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| 作者单位： | 广西师范学院,计算机与信息工程学院,广西,南宁,530001 |
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| 基金项目： | 国家自然科学基金，广西自然科学基金，广西高等学校优秀人才资助计划项目 |
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| 摘 要： | RBF神经网络作为一种采用局部调节来执行函数映射的人工神经网络,在逼近能力、分类能力和学习速度等方面都有良好的表现,但由于RBF网络的隐节点的个数和隐节点的中心难以确定,从而影响了整个网络的精度,极大地制约了该网络的广泛应用.为此本文提出基于GEP优化的RBF神经网络算法,对其中心向量及连接权值进行优化.实验表明,本文所提算法比RBF算法的预测误差平均减少了48.96% .
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| 关 键 词： | k-均值算法 人工神经网络 |
Enhanced RBF Neural Network Algorithm Based on Gene Expression Programming |
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| TAO Jun-jian,YUAN Chang-an,CAI Hong-guo.Enhanced RBF Neural Network Algorithm Based on Gene Expression Programming[J].Mini-micro Systems,2010,31(5). |
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| Authors: | TAO Jun-jian YUAN Chang-an CAI Hong-guo |
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| Affiliation: | TAO Jun-jian,YUAN Chang-an,CAI Hong-guo(College of Computer , Information Engineering,Guangxi Teachers Education University,Nanning 530001,China) |
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| Abstract: | RBF neural network,as an artificial neural network which adopts partial regulation to implement function mapping,specializes in approximation capability,classification capability and learning speed. But because of the difficulty of confirming the number of hidden nodes and the center of hidden nodes,it affects the accuracy of the entire network,which greatly restricted the use of the network RBF. This paper proposes Radial Basis Function Algorithms based on Gene Expression Programming (GEP-RBFA),by taking a... |
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| Keywords: | GEP RBF |
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