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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:

65L09 Numerical solution of inverse problems involving ordinary differential equations
34A55 Inverse problems involving ordinary differential equations
65L05 Numerical methods for initial value problems involving ordinary differential equations
65K05 Numerical mathematical programming methods
90C15 Stochastic programming
37D45 Strange attractors, chaotic dynamics of systems with hyperbolic behavior
65P20 Numerical chaos
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References:

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