  A simple yet effective architectural design of radial basis function neural
networks (RBFNN) makes them amongst the most popular conventional neural
networks. The current generation of radial basis function neural network is
equipped with multiple kernels which provide significant performance benefits
compared to the previous generation using only a single kernel. In existing
multi-kernel RBF algorithms, multi-kernel is formed by the convex combination
of the base/primary kernels. In this paper, we propose a novel multi-kernel
RBFNN in which every base kernel has its own (local) weight. This novel
flexibility in the network provides better performance such as faster
convergence rate, better local minima and resilience against stucking in poor
local minima. These performance gains are achieved at a competitive
computational complexity compared to the contemporary multi-kernel RBF
algorithms. The proposed algorithm is thoroughly analysed for performance gain
using mathematical and graphical illustrations and also evaluated on three
different types of problems namely: (i) pattern classification, (ii) system
identification and (iii) function approximation. Empirical results clearly show
the superiority of the proposed algorithm compared to the existing
state-of-the-art multi-kernel approaches.
