Parameter estimation for fractional-order chaotic systems by improved bird swarm optimization algorithm

Zhang, Pei; Yang, Renyu; Yang, Renhuan; Ren, Gong; Yang, Xiuzeng; Xu, Chuangbiao; Bao-guo, XU; Zhang, Huatao; Cai, Yanning; Lu, Yue

doi:10.1142/s0129183119500864

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…In that case, a feedback controller was designed and an improved quantum PSO algorithm was proposed to optimize the controller. An improved bird swarm algorithm was proposed in [25], and a fractional chaotic system and a fractional Lorentz system were chosen as two examples for parameter estimation. In [26], a fractional-order chaotic system parameter estimator was proposed based on differential evolution, which treated the order as an additional parameter, and estimated the parameters and the order together by minimizing an objective function.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Fan

2023

IEEE Access

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：Parameter identification of fractional-order chaotic power systems is a multidimensional optimization problem that plays a decisive role in the synchronization and control of fractional-order chaotic power systems. In this paper, a state transition algorithm based on the lens imaging learning strategy is proposed for parameter identification of fractional-order chaotic power systems. Taking a fractional-order six-dimensional chaotic power system mathematical model as an example, the mathematical model and chaotic state are analyzed. First, the Tent chaotic mapping is used to initialize the population, thus increasing population diversity. The randomness and ergodicity of the Tent chaotic sequence are used to enhance the global searching ability of the algorithm. Second, a maturity index is employed to determine population maturity. The lens imaging learning strategy is used to suppress the premature convergence of the state transition algorithm effectively and help the population jump out of local optima. Finally, the improved state transition algorithm is used to identify the parameters of the fractional-order six-dimensional chaotic power system model. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition and particle swarm optimization algorithms. The simulation results show that the parameters of the fractional-order chaotic power system are identified accurately even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Fan

2023

IEEE Access

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“…The results show that the algorithm combined with a logistic map has the best performance. In addition, the JAYA algorithm [15], differential evolution algorithm [16], particle swarm optimization algorithm [17], bird swarm algorithm [18], sunflower optimization algorithm [19], butterfly optimization algorithm [20], and other intelligent optimization algorithms are utilized to identify the parameters of nonlinear systems with chaotic behavior.…”

Section: Introductionmentioning

confidence: 99%

Research on Information Identification of Chaotic Map with Multi-Stability

Li,

Peng

2023

Fractal Fract

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Influenced by the rapid development of artificial intelligence, the identification of chaotic systems with intelligent optimization algorithms has received widespread attention in recent years. This paper focuses on the intelligent information identification of chaotic maps with multi-stability properties, and an improved sparrow search algorithm is proposed as the identification algorithm. Numerical simulations show that different initial values can lead to the same dynamic behavior, making it impossible to stably and accurately identify the initial values of multi-stability chaotic maps. An identification scheme without considering the initial values is proposed for solving this problem, and simulations demonstrate that the proposed method has the highest identification precision among seven existing intelligent algorithms and a certain degree of noise resistance. In addition, the above research reveals that chaotic systems with multi-stability may have more potential applications in fields such as secure communication.

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“…Because swarm intelligence (SI) optimization algorithm does not need the derivative information of objective function, it has more advantages than traditional optimization algorithm in parameter identification. At present, many research results have emerged for some classical continuous memristive chaotic systems, such as particle swarm optimization algorithm [14,15], differential evolution algorithm [16,17], artificial bee colony optimization algorithm [18], bird swarm algorithm [19], Jaya algorithm [20]. For a discrete memristive chaotic system, the swarm intelligence optimization algorithms also provide many practical solutions, such as an improved PSO algorithm [21], meta-heuristic algorithm [22], and enhanced differential evolution algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm

Xiong

Shen²,

Bing³

et al. 2022

Front. Phys.

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A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto Sparrow Search Algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers’ location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers’ position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.

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Parameter estimation for fractional-order chaotic systems by improved bird swarm optimization algorithm

Cited by 11 publications

References 25 publications

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Research on Information Identification of Chaotic Map with Multi-Stability

Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm

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