基于敏感度方差重要性的RBF神经网络结构优化算法  

Optimal algorithm for RBF neural network structure based on variance significance in output sensitivity

作　　者：杨一[1] 高社生[1] 胡高歌[1] 

机构地区：[1]西北工业大学自动化学院,西安710072

出　　处：《控制与决策》2015年第8期1393-1398,共6页Control and Decision

基　　金：国家自然科学基金项目(61174193);航天科技创新基金项目(CASC201102)

摘　　要：针对RBF神经网络的结构设计问题,提出一种基于输出敏感度方差重要性的结构优化算法.首先,检验网络隐层节点的输出敏感度在样本集上的方差是否与零有显著差异,以此作为依据增加或删除相应的隐层节点;然后,对调整后的网络参数进行修正,使网络具有更好的拟合精度和收敛性;最后,对所提出的优化算法进行仿真实验,结果表明,所提出的算法可根据研究对象自适应地调整RBF的网络结构,具有良好的逼近能力和泛化能力.Aiming at the problem of design of the RBF neural network structure, an optimal algorithm based on variance significance in output sensitivity is proposed. Firstly, it is tested whether the variance in output sensitivity for the different patterns is significantly different from zero. If the variance in output sensitivities is significantly different from zero or not, the hidden units corresponding can be inserted or pruned. Then, the gradient descent method for the parameter adjusting ensures the fitting precision of the network. Finally, the proposed optimal algorithm is applied to the simulation experiment. Simulation results show that the proposed optimal algorithm can adjust network structure adaptive and possess good approximation and generalization ability.

关 键 词：径向基函数神经网络 输出敏感度 方差重要性 结构优化算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]