Fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training. (5th March 2021)

• Record Type: Journal Article
• Authors: Gao, Runxia; Jiang, Houping
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 1(2021)
• Page Start: 48
• Page End: 55
• Publication Date: 2021-03-05
• Subjects: fuzzy control -- observation error -- ensemble transform Kalman filter -- ETKF; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modelling process, combined with dynamic particle swarm optimisation training algorithm, the traditional fuzzy control mathematical model based on the Nash equilibrium solution method is difficult to converge to the optimal solution of the state space, leading to bad control performance. This paper proposes the fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training, constructs the general structure model of fuzzy control, describes the standard particle algorithm under the constraint of learning samples of random functional, obtains the global optimal solution of control domain of fuzzy control parameters, and conducts particle swarm optimisation training by adopting the position vector fitness updating method. The research results show that the new method can make every step state update get more effective observation information, reduce the error caused by the difficult use of observation data, reduce the computation cost and improve the accuracy of fuzzy control.