[1]LIU Sheng,SONG Jia,LI Gao-yun.Modeling a complex nonlinear system with particle swarm optimizationand paralleloptimized least squares support vector regression[J].CAAI Transactions on Intelligent Systems,2010,5(1):51-56.
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Modeling a complex nonlinear system with particle swarm optimizationand paralleloptimized least squares support vector regression

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 1 Page number: 51-56 Column: 学术论文—人工智能基础 Public date: 2010-02-25
Title:
Modeling a complex nonlinear system with particle swarm optimizationand paralleloptimized least squares support vector regression
Author(s):
LIU Sheng;  SONG Jia;  LI Gao-yun
College of Automation， Harbin Engineering University, Harbin 150001, China
Keywords:
particle swarm optimization;  least squares support vector regression;  nonlinear system identification;  black box model;  ship maneuvering
CLC:
N945.1；TP18
DOI:
-
Abstract:
Complex nonlinear systems usually suffer from highorder nonlinearity and uncertainty of parameters. This makes it difficult to establish an accurate mathematical model using conventional identification methods. To solve this problem, a new least squares support vector regression based on particle swarm optimization (PSOLSSVR) was proposed. This identification model used two PSOs in parallel. One automatically sets the parameters of the LSSVR, while the other iterates the matrix. Thus the precision of identification is ensured, and calculation speed is improved by avoiding matrix inversion. This method was employed in dynamic identification of ship steering. Simulations proved that the PSOLSSVR has a simple structure, high precision of model identification.
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Last Update: 2010-03-31

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