Parameter estimation of bilinear systems based on an adaptive particle swarm optimization

H Modares, A Alfi, MBN Sistani - Engineering Applications of Artificial …, 2010 - Elsevier
Engineering Applications of Artificial Intelligence, 2010Elsevier
Bilinear models can approximate a large class of nonlinear systems adequately and usually
with considerable parsimony in the number of coefficients required. This paper presents the
application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online
parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm
Optimization (APSO) is proposed to increase the convergence speed and accuracy of the
basic particle swarm optimization to save tremendous computation time. An illustrative …
Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to increase the convergence speed and accuracy of the basic particle swarm optimization to save tremendous computation time. An illustrative example for the modeling of bilinear systems is provided to confirm the validity, as compared with the Genetic Algorithm (GA), Linearly Decreasing Inertia Weight PSO (LDW-PSO), Nonlinear Inertia Weight PSO (NDW-PSO) and Dynamic Inertia Weight PSO (DIW-PSO) in terms of parameter accuracy and convergence speed. Second, APSO is also improved to detect and determine varying parameters. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a good promising particle swarm optimization algorithm for online parameter estimation.
Elsevier
Showing the best result for this search. See all results