Parameter Self-regulation REM Algorithm Based on RBF Neural Network

Aiming at the shortcoming of random exponential marking (REM) algorithm, whose parameters are difficultly regulated, this paper proposes a new RBF neuralnetworkbased REM algorithm in which REM parameters are tuned via RBF network technique. By approximating the REM algorithm via PI control under certain conditions, the regulation of REM parameters is equivalent to adjust the coefficients P and I. Thus, the process of regulation may be simplified and the real time control performance may be promoted. The simulation results show that the proposed algorithm can effectively adapt to dynamic network environments, and attain quicker adjusting.