Analysis of Minimal Radial Basis Function Network Algorithm for Real-Time Identification of Nonlinear Dynamic Systems

This paper presents first a performance analysis of the recently developed Minimal Resource Allocating Network (MRAN) algorithm for on-line identification of nonlinear dynamic systems. Using nonlinear time invariant and time varying identification benchmark problems, MRAN’s performance is compared with the recently proposed On-line Structural Adaptive Hybrid Learning (ONSAHL) algorithm of Junge and Unbehauen. The results indicate that MRAN realizes networks using fewer hidden neurons than ONSAHL algorithm with better approximation accuracy. Next, methods for improving the run time performance of MRAN for real time identification of the nonlinear systems are developed. An extension to MRAN referred to as the Extended Minimum Resource Allocating Network (EMRAN) which utilizes a winner neuron strategy is highlighted. This modification reduces the computation load for MRAN and leads to considerable reduction in the learning time with only a slight increase in the approximation error. Using the same benchmark problems, the results show that EMRAN is well suited for fast on-line identification of nonlinear plants.