Nonlinear analysis and minimum L2-norm control in memcapacitor-based hyperchaotic system via online particle swarm optimization

In this paper, we propose a hyperchaotic image encryption system based on particle swarm optimization algorithm (PSO) and cellular automata (CA). Firstly, to improve the ability to resist plaintext attacks, the initial conditions of the hyperchaotic system are generated by the hash function value which is closely related to the plaintext image to be encrypted. In addition, the fitness of PSO is the correlation coefficient between adjacent pixels of the image. Moreover, On the basis of hyperchaotic system, cellular automata technology is adopted, which can enhance the randomness of population distribution and increase the complexity and diversity of the population so that the security of the encryption system can be improved and avoid falling into local optimum. The simulation results and security analysis of the proposed encryption system demonstrate that the hyperchaotic image encryption system has high resistance against plaintext attack and statistical attack.

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A NEW ALGORITHM IN NONLINEAR ANALYSIS OF STRUCTURES USING PARTICLE SWARM OPTIMIZATION

IIUM Engineering Journal ◽

10.31436/iiumej.v17i2.581 ◽

2016 ◽

Vol 17 (2) ◽

pp. 157-168 ◽

Cited By ~ 1

Author(s):

Iman Mansouri ◽

Ali Shahri ◽

Hassan Zahedifar

Keyword(s):

Particle Swarm Optimization ◽

Nonlinear Analysis ◽

Nonlinear Equations ◽

Particle Swarm ◽

Load Ratio ◽

Buckling Analysis ◽

Displacement Control ◽

Swarm Optimization ◽

Post Buckling ◽

Limit Points

Solving systems of nonlinear equations is a difficult problem in numerical computation. Probably the best known and most widely used algorithm to solve a system of nonlinear equations is Newton-Raphson method. A significant shortcoming of this method becomes apparent when attempting to solve problems with limit points. Once a fixed load is defined in the first step, there is no way to modify the load vector should a limit point occur within the increment. To overcome this defect, displacement control methods for passing limit points can be used. In displacement control method, the load ratio in the first step of an increment is defined so that a particular key displacement component will change only by a prescribed amount. In this paper the load ratio is obtained using particle swarm optimization (PSO) algorithm so that the complex behavior of structures can be followed, automatically. Design variable is load ratio and its unbalanced force is also considered as objective function in optimization process. Numerical results are performed under geometrical nonlinear analysis, elastic post-buckling analysis and inelastic post-buckling analysis. The efficiency and accuracy of proposed method are demonstrated by solving these examples.Â Â

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Geometrically Nonlinear Analysis of Trusses Using Particle Swarm Optimization

Studies in Computational Intelligence - Recent Advances in Swarm Intelligence and Evolutionary Computation ◽

10.1007/978-3-319-13826-8_15 ◽

2014 ◽

pp. 283-300 ◽

Cited By ~ 5

Author(s):

Rasim Temür ◽

Yusuf Sait Türkan ◽

Yusuf Cengiz Toklu

Keyword(s):

Particle Swarm Optimization ◽

Nonlinear Analysis ◽

Particle Swarm ◽

Geometrically Nonlinear ◽

Swarm Optimization ◽

Geometrically Nonlinear Analysis

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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