Title :
A Novel Particle Swarm-based Fuzzy Control Scheme
Author_Institution :
Menoufia Univ., Menoufia
Abstract :
Auto Regressive Moving Average (ARMA) models have attractive properties of simple computations and fast learning. However, the unknown structures of the processes to be modeled produces difficult harmony between them and their ARMA models. This paper develops a novel ARMA model using the Particle Swarm Algorithm (PSA) to overcome this discord problem. It also introduces a PSA-based fuzzy control scheme to overcome the limitations of the use of conventional controllers. The PSA is not only used to structure the ARMA model of the process being controlled but also employed to optimize the parameters of the proposed control scheme. This paper also implements the proposed scheme for controlling real time processes.
Keywords :
autoregressive moving average processes; fuzzy control; particle swarm optimisation; ARMA; PI control; auto regressive moving average model; fuzzy control; fuzzy set theory; particle swarm algorithm; Birds; Design optimization; Educational institutions; Fuzzy control; Marine animals; Neural networks; Particle swarm optimization; Power system modeling; Process control; Recurrent neural networks;
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
DOI :
10.1109/FUZZY.2006.1681969