摘要
针对k平均聚类径向基(Radial Basis Function简称RBF)网络算法的聚类结果易受初始参数选取的影响,并常收敛于局部极小值的问题,提出一种将蚁群优化算法用于径向基神经网络训练过程,优化径向基函数的中心点,建立相应优化模型的算法.实验结果表明,该算法精确度高于k平均聚类径向基神经网络算法,且函数的拟合程度也得到了改善.
To settle the problem that the cluster results of k-meanclustering Radial Basis Function （RBF） was easy to be influenced by selection of initial characters and converge to local minimum, Ant Colony Optimization for the RBF neural networks and a model based on this method were presented in this paper. Compared with k-mean clustering RBF Algorithm, the result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function （RBF） neural networks is higher, and the extent of fitting has been improved.
出处
《哈尔滨理工大学学报》
CAS
2008年第1期56-58,62,共4页
Journal of Harbin University of Science and Technology
基金
黑龙江省自然科学基金项目(F0316)
哈尔滨市学科后备带头人基金项目(2005AFXXJ020)
