To overcome the limitation of one single kernel in the traditional kernel function model

a new type of mixture kernel is constructed by combining two kernel functions linearly.Each individual regressor in RBF network is trained term by term using orthogonal least squares algorithm.In the training phase

a global

k

-means cluster algorithm is used to decide the kernel centers.For each clustering centers

Group search optimizer (GSO) is utilized to get the proper scale and weight within the kernel.The optimal kernel parameters which minimize the training error are used to shape each regressor.The experiments results show that the new RBF network is sparser than some traditional RBF network with one single kernel.