Abstract
Improved Particle Swarm Optimization (IPSO), which is a new robust stochastic evolutionary computation algorithm based on the movement and intelligence of swarms, is proposed to estimate parameters of nonlinear dynamical systems. The effectiveness of the IPSO algorithms is compared with Genetic Algorithms (GAs) and standard Particle Swarm Optimization (PSO). Simulation results of two kinds of nonlinear dynamical systems will be illustrated to show that the more accurate estimations can be achieved by using the IPSO method.
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Ye, M. (2006). Parameter Identification of Dynamical Systems Based on Improved Particle Swarm Optimization. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_42
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DOI: https://doi.org/10.1007/978-3-540-37256-1_42
Publisher Name: Springer, Berlin, Heidelberg
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