Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm |
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| Authors: | Yu Huang Feng Guo Yongling Li Yufeng Liu |
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| Affiliation: | 1. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding, China.; 2. Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen, China.; 3. State Key Laboratory of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing, China.; University of Chinese Academy of Sciences, CHINA, |
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| Abstract: | Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. |
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