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Parameters identification of chaotic systems by quantum-behaved particle swarm optimization. (English) Zbl 1181.65103
Summary: We apply a novel evolutionary optimization algorithm named quantum-behaved particle swarm optimization (QPSO) to estimate the parameters of chaotic systems, which can be formulated as a multimodal numerical optimization problem with high dimension from the viewpoint of optimization. Moreover, in order to improve the performance of QPSO, an adaptive mechanism is introduced for the parameter beta of QPSO. Finally, numerical simulations are provided to show the effectiveness and efficiency of the modified QPSO method.

MSC:
65L09Inverse problems for ODE (numerical methods)
34A55Inverse problems of ODE
65L05Initial value problems for ODE (numerical methods)
65K05Mathematical programming (numerical methods)
90C15Stochastic programming
37D45Strange attractors, chaotic dynamics
65P20Numerical chaos