RBF网络的鲁棒最小二乘学习算法

RBF network learning algorithm using robust least-squares

首先,针对径向基函数(RBF)神经网络参数学习中最小二乘法(LS)难以获得较高鲁棒性的问题,假定训练数据扰动上界可知,并基于鲁棒最小二乘原理,提出一种RBF网的最优鲁棒参数学习算法;然后分析指出,扰动上界可依据训练数据集自适应学习估计;最后通过实验分析结果表明了所提算法具有较高的参数鲁棒学习能力.与LS相似,新算法无额外参数,易于实际应用.

How to obtain the high robustness in radius basis function（RBF） learning is a trouble.Therefore,with the supposition that the perturbation of training dataset is bounded,a RBF network learning algorithm is proposed in terms of the robust least-square principle.Moreover,a strategy of estimating perturbation bound is proposed.Experimental analysis shows that the proposed method has effective robust learning performance.Similar to standard least square algorithm,no additional parameters are needed for the proposed algorithm,which is benefit to more practical applications.