Identification of Uncertain Incommensurate Fractional-Order Chaotic Systems Using an Improved Quantum-Behaved Particle Swarm Optimization Algorithm

Wei, Jiamin; Yu, Yongguang

doi:10.1115/1.4039582

Cited by 14 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Theoretically, there are two broad optimization categories; these are derivative-free and gradient-based. For the derivative-free methods, there are the direct-search methods, consisting of particle swarm optimization (PSO) [146,147], etc. For the gradient-based methods, there are gradient descent and its variants.…”

Section: Optimal Machine Learning and Optimal Randomnessmentioning

confidence: 99%

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Niu

Chen

West

2021

Entropy

View full text Add to dashboard Cite

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.

show abstract

Section: Optimal Machine Learning and Optimal Randomnessmentioning

confidence: 99%

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Niu

Chen

West

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Some researchers have further improved the PSO algorithm for the local optimum problem, such as the QPSO algorithm inspired by quantum mechanics theory. 27,28) The QPSO technique simply needs to update location information and does not need to calculate particle velocity. This makes the method has the advantages of simple update equations, fewer control parameters, and fast convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

Ding,

Shen,

Xie

2024

ISIJ Int.

View full text Add to dashboard Cite

Taking full account of the complex mechanism of the continuous annealing furnace (CAF) production process and the difficulty in establishing an accurate mathematical model, this paper adopts a quantum particle swarm optimization (QPSO) algorithm to optimize the parameters of the radial basis function (RBF) neural network which used to predict the model of strip temperature in CAF. Firstly, to improve the accuracy of modeling, the input and output variables of RBF neural network model prediction are determined by analyzing the mechanism model of the heating section of the CAF and the factors affecting the strip temperature.Secondly, due to the trial and error method used for parameter selection in RBF neural network, which results in low work efficiency and difficulty in selecting the optimal value, the QPSO algorithm is introduced to search for the optimal solution. Furthermore, to avoid encountering local optimal issues and improve searching performance, an improved QPSO algorithm that combines the characteristics of generalized opposition-based learning and differential evolution is proposed. Finally, by collecting the production site data of a large-scale CAF, experiments are carried out to verify the effectiveness of the proposed methods.

show abstract

“…However, with the development of swarm intelligence optimization algorithms, many intelligent algorithms have been applied to the parameter identification of typical fractional chaotic systems. In [19], an improved quantum behavioral particle swarm optimization (PSO) algorithm was proposed for the parameter estimation problem of uncertain fractional-order chaotic systems, and the system parameters and fractional-order derivatives were estimated as independent unknown parameters. Parameter estimation of fractional-order chaotic systems with time delay was studied in [20], which is of great significance to such systems' modeling and control.…”

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

View full text Add to dashboard Cite

：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.

show abstract

Identification of Uncertain Incommensurate Fractional-Order Chaotic Systems Using an Improved Quantum-Behaved Particle Swarm Optimization Algorithm

Cited by 14 publications

References 35 publications

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

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

Contact Info

Product

Resources

About