Title :
Nonlinear System Identification using Locally Linear Model Tree and Particle Swarm Optimization
Author :
Nekoui, Mohammad Ali ; Sajadifar, Seyed Mohammad
Author_Institution :
K. N. Toosi Univ. of Technol., Tehran
Abstract :
Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve fault-tolerant behavior. Locally linear model tree algorithm provides a very efficient way to identify the dynamic system nonlinearities. In this paper particle swarm optimization is used to optimize the structure of LOLIMOT algorithm. Simulation results show the effectiveness of the proposed extension of the original LOLIMOT to have a good precise with optimal number of neurons.
Keywords :
nonlinear systems; particle swarm optimisation; time-varying systems; trees (mathematics); LOLIMOT algorithm; dynamic system nonlinearities; locally linear model tree; nonlinear system identification; particle swarm optimization; Electrical equipment industry; Evolutionary computation; Fault diagnosis; Fault tolerant systems; Neurons; Nonlinear control systems; Nonlinear systems; Particle swarm optimization; Power system modeling; Time varying systems; Locally Linear Model Tree (LOLIMOT); Nonlinear System Identification; Particle Swarm Optimization (PSO);
Conference_Titel :
Industrial Technology, 2006. ICIT 2006. IEEE International Conference on
Conference_Location :
Mumbai
Print_ISBN :
1-4244-0726-5
Electronic_ISBN :
1-4244-0726-5
DOI :
10.1109/ICIT.2006.372461
