Real-time performance evaluation of the minimal radial basis function network for identification of time varying nonlinear systems

Abstract

This paper investigates the real-time performance of the recently developed minimal radial basis function (RBF) network called the minimal resource allocation network (MRAN) on a time varying signal identification problem. MRAN is a sequential learning RBF network and has the ability to grow and prune the hidden neurons to ensure a parsimonious structure. Because of its ability to dynamically adjust its size, MRAN is well suited for real-time signal processing applications. In this paper the ability of MRAN to identify, in real time, a time varying signal is investigated experimentally. In the experiments the sampling rate is varied from low to high values. When the sampling rate increases beyond a limit, the performance of MRAN starts to deteriorate as the time to perform the various calculations in the algorithm begin to exceed the sampling interval. A modification to MRAN that greatly reduces its computational load with very little increase in the overall error is then presented as the real-time alternative to the original algorithm. The real-time MRAN is shown to cope well with smaller sampling intervals for the same time varying signal identification problem.