自构造RBF神经网络及其参数优化  

Self-growing RBF Neural Networks and Parameters Optimization

作　　者：兰天鸽[1] 方勇华[1] 熊伟[1] 孔超[1] 

机构地区：[1]中国科学院安徽光学精密机械研究所遥感研究室,合肥230031

出　　处：《计算机工程》2008年第9期200-202,共3页Computer Engineering

摘　　要：径向基函数神经网络的构造需要确定每个RBF的中心、宽度和数目。该文利用改进的聚类算法自动构造RBFN,考虑样本的类别属性,根据样本分布自动计算RBF的中心和宽度,并确定RBF的数目。所有的网络参数采用非线性优化算法来优化。通过IRIS分类问题和混沌时间序列预测评价自构建RBFN的性能,验证参数优化效果。结果表明,自构造RBFN不但能够自动确定网络结构,而且具有良好的模式分类和函数逼近能力。通过对网络参数的非线性优化,该算法明显改善了网络性能。Construction of Radiai Basis Function Neural（RBFN） networks involves computation of centers, widths of each RBF, and number of RBF in the middle layer. The modified clustering algorithm is used to construct RBFN automaticaily. The algorithm considers the class membership of training samples, can automaticai!y compute RBF centers and widths, and determines the number of Radiai Basis Function（RBF） units based on the distribution of samples. Parameters are optimized with nonlinear optimization technique. The performance Of the self-growing RBFN and effects of the optimization are estimated with IRIS classification problem and chaotic time series prediction. The results confirm that self-growing RBFN determines networks structure automatically, and has good performance in pattern recognition and function approximation. Better performance can be observed after nonlinear optimization of networks parameters.

关 键 词：径向基函数 自构造网络 参数优化 模式识别 混沌时间序列 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]