A Direct Link Minimal Resource Allocation Network for Adaptive Noise Cancellation

Abstract

This paper presents a modification to the Minimal Resource Allocation Network (MRAN) of Yingwei et al. by introducing direct links from inputs to output and investigates its performance for noise cancellation problems. MRAN has the same structure as a Radial Basis Function network but uses a sequential learning algorithm that adds and prunes hidden neurons as input data is received sequentially so as to produce a parsimonious network. Earlier work by Sun Yonghong et al. has demonstrated the capability of MRAN to produce a compact network with excellent noise reduction properties. In this paper the capability of the direct link Minimal Resource Allocation Network (DMRAN) is evaluated by comparing it with MRAN on several nonlinear adaptive noise cancellation problems. The direct link MRAN uses the same learning algorithm as MRAN but with the introduction of direct links we are able to realise even smaller networks than MRAN with better noise reduction properties.