Recursive orthogonal least squares learning with automatic weight selection for Gaussian neural networks

Gaussian neural networks have always suffered from the curse of dimensionality; the number of weights needed increases exponentially with the number of inputs and outputs. Many methods have been proposed to solve this problem by optimally or sub-optimally selecting the weights or centers of the Gaussian neural network. However, most of these attempts are not suitable for online implementation. In this paper, we develop a recursive orthogonal least squares learning with automatic weight selection (ROLS-AWS) for a two-layered Gaussian neural network. This ROLS-AWS algorithm is capable of selecting useful weights sub-optimally and recursively. In doing so, we will not only reduce the growth of the size of the weights but also minimizes the number of weights used. Due to the recursive nature of this algorithm, it can be applied to any online system, as in control and signal processing applications.