A FPN Parameter Optimization Method Based on CDEDPSO

Abstract

Abstract. For the uncertain change characteristics of transition matrix parameters in complex FPN system,this paper presents a parameters optimization algorithm based on CDEDPSO.This article focuses on the weight, threshold and certainty of FPN optimized model and these parameters are analyzed, improved and simulated simultaneously.This method implements dynamic programming and self-learning of uncertain characterization parameters in FPN.Finally, in this paper, the method is applied to a FPN model and the feasibility and effectiveness of this method has been verified. IntroductionThe transition matrix in FPN model is a collection of trigger condition parameters and the biggest drawback is the poor self-learning ability for the uncertain parameters.The changes feature of uncertain parameters are reflected in these areas:the determination of weight, threshold, certainty and other parameters.Therefore,Consider how to deal with and improve uncertain parameters of FPN to optimize FPN uncertain knowledge representation model,enhance the dynamic programming and self-learning ability of FPN and improve the efficiency and accuracy of FPN model has became an important research topic