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Intelligent Systems and Computer Vision (ISCV)

作者： Aarab, Ilham Madani, Abdellah Kissi, Mohamed Chouaib Doukkali Univ Fac Sci Dept Comp LAROSERI BP 20 El Jadida 24000 Morocco Chouaib Doukkali Univ Fac Sci Dept Comp EMMID El Jadida 24000 Morocco

ISBN: (纸本)9781479975112

Among raised solutions for global flow optimization in traffic jam, we can quote traffic lights optimization. Several researches treated this problem and proposed different models to solve it. In this paper, we will use the cellular automaton to model the traffic flow in crossroads and the self-organizing method to control traffic lights and then we will apply the pso algorithm to get the appropriate values of parameters for self-organizing method. We will demonstrate that pso algorithm provides an optimal solution in order to improve the global flow.

关键词： cellular automaton models self-organizing method pso algorithm

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An Improved pso algorithm and Its Application on Model Identification of Coarse-stage in Lithography Machine 9

An Improved PSO Algorithm and Its Application on Model Ident...

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9th International Symposium on Precision Engineering Measurements and Instrumentation (ISPEMI)

作者： Liu Yang Liu Fazhi Wei Kai Fu Zhenxian Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Ultra Precis Optoelect Instrumentat Engn Ctr Harbin 150001 Peoples R China

ISBN: (纸本)9781628415612

Lithography machine is a high-precision manufacturing equipment of electromechanical integration, involving precision machinery, precision control and measure, precision measurement and other subjects. pso algorithm is easy to fall into local optimum, which has simple concepts and fast convergence. This paper proposes an improved pso algorithm with adaptive parameters and boundary constraint, in ensuring accuracy of the algorithm optimization and fast convergence. Simulation results show that the methods have good applicability and stability for coarse-stage in lithpgraphy machine.

关键词： Lithography machine Model identification pso algorithm

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Cloud Data Migration Method Based On pso algorithm 14

Cloud Data Migration Method Based On PSO Algorithm

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Geng Yushui Yuan Jiaheng Qilu Univ Technol Sch Informat Jinan Peoples R China

ISBN: (纸本)9781467365932

Cloud storage system can play an important role in large-scale, and it supports high-performance cloud applications. To cloud storage systems, data migration is key technology to realize the nodes dynamically extensible and elastic load balancing. How to reduce migration cost of time is the problem that cloud service providers need to solve. Existing research efforts were focused on the data migration issues under the non-virtualized environments, which often do not applicable to cloud storage systems. In response to these challenges, we put data migration issues into the loadbalancing scenarios to solve. We propose an algorithm based on particle swarm optimization algorithm which can reduces the cost of time. In the experiment, we can use Yahoo services benchmarking YCSB tool which could verify the validity of the method. It is a test framework designed to help users understand the different cloud computing, database performance.

关键词： Cloud data Data migration pso algorithm Load balance

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A Parameters Identification Method for Hammerstein Systems Based on pso algorithm 27

A Parameters Identification Method for Hammerstein Systems B...

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27th Chinese Control and Decision Conference (CCDC)

作者： Wang, Rui Mao, Zhizhong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479970162

The non-linear static components of Hammerstein systems are described by method of polynomial and piecewise linearization method. First, parameters which need to identify through mathematical derivation can be obtained. Then pso algorithm is used to identify Hammerstein system parameters. Computer simulations demonstrate that parameters identification method based on pso algorithm has strong robustness to measuring noise and uncertain models.

关键词： Hammerstein system Parameter identification pso algorithm

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A Novel AVR System Utilizing Fuzzy PIDF Enriched by FOPD Controller Optimized via pso and Sand Cat Swarm Optimization algorithms

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ENERGIES 2025年第6期18卷 1337-1337页

作者： Shouran, Mokhtar Alenezi, Mohammed Muftah, Mohamed Naji Almarimi, Abdalmajid Abdallah, Abdalghani Massoud, Jabir Libyan Ctr Engn Res & Informat Technol POB 38645 Bani Wali Libya Coll Elect Technol POB 38645 Bani Walid Libya Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional-Integral-Derivative controller with Filtered derivative action (PIDF) to propose a hybrid Fuzzy PIDF controller enhanced by Fractional-Order Proportional-Derivative (FOPD) for AVR applications. For the first time, the newly introduced Sand Cat Swarm Optimization (SCSO) algorithm was applied to the AVR system to tune the parameters of the proposed fuzzy controller. The SCSO algorithm has been recognized as a powerful optimization tool and has demonstrated success across various engineering applications. The well-known Particle Swarm Optimization (pso) algorithm was also utilized in this study to optimize the gains of the proposed controller. The Fuzzy PIDF plus FOPD is a novel configuration that is designed to be a robust control technique for AVR to achieve an excellent performance. In this research, the Fuzzy PIDF + FOPD controller was optimized using the pso and SCSO algorithms by minimizing the Integral Time Absolute Error (ITAE) objective function to enhance the overall performance of AVR systems. A comparative analysis was conducted to evaluate the superiority of the proposed approach by benchmarking the results against those of other controllers reported in the literature. Furthermore, the robustness of the controller was assessed under parametric uncertainties and varying load disturbances. Also, its robustness was examined against disturbances in the control signal. The results demonstrate that the proposed Fuzzy PIDF + FOPD controller tuned by the pso and SCSO algorithms delivers exceptional performance as an AVR controller, outperforming other controllers. Additionally, the findings confirm the robustness of the Fuzzy PIDF + FOPD controller against parametric uncertainties, establishing its potential for a successfu

关键词： AVR pso algorithm SCSO algorithm fuzzy PIDF plus FOPD controller ITAE

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PM2.5 measuring method using RBF neural network combined with pso algorithm 27

PM2.5 measuring method using RBF neural network combined wit...

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27th Chinese Control and Decision Conference, CCDC 2015

作者： Lin, Xu Guan, Tian-Yi Li, Yan-Nong Zheng, Wen-Jing Guo, Jing-Yi College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781479970179

Considering the disadvantage of PM2.5 detection devices, there are many problems such as the low automation, poor test repeatability, and dielectric material loss etc. This paper presents a new PM2.5 detection system using laser diffraction technique based Fraunhefor diffraction theory. The Radial Basis Function (RBF) neural network with the inputs of multiple laser diffraction signals is used to be the micro particles calculating model to improve the detecting precision. To tackle the problems in the training algorithms, the Particle Swarm Optimization (pso) algorithm is employed to optimize the key parameters of the RBF neural network (RBFNN). The simulation and experiment results show that the new PM2.5 detection system satisfies the detection requirements with the high calculating precision, and effectively overcomes the problems in the conventional detection system. © 2015 IEEE.

关键词： Laser diffraction PM2.5 detection pso algorithm RBF neural network

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Prediction and optimization of Aluminum content in Ni-P-Al2O3 composite coatings on GCr15 Steel Based on pso-BP Neural Network

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SURFACES AND INTERFACES 2025年 64卷

作者： Mei, Shunqi Xiao, Zhi Chen, Zhen Hu, Zekui Zou, Xiaohui Xu, Qiao Zheng, Quan Wuhan Text Univ Hubei Digital Text Equipment Key Lab Wuhan 430200 Hubei Peoples R China Jianhu Lab Adv Text Technol Innovat Ctr Shaoxing 312000 Peoples R China Xian Polytech Univ Sch Mech & Elect Engn Xian 710048 Peoples R China

This paper adopts a neural network-based surface fitting technique to explore the factors influencing the aluminum content in the Ni-P-Al2O3 coatings on GCr15 bearing steel surfaces, and the impact of the aluminum content on the coating properties. Temperature, pH value, surfactant concentration, and Al2O3 concentration were taken as input parameters for the neural network, with aluminum content as the output parameter. The factors affecting the aluminum content in the coatings were modeled using a pso-BP neural network, and the output results of the model were validated through experiments. The results indicate that the pH value and surfactant content have the strongest correlation with the aluminum content in the coating. An increase in pH value promotes the stability of the plating solution, while an increase in surfactant concentration enhances the aluminum content in the coating. The pso-BP model demonstrated a small root mean square error, and the predicted optimal process parameters were a pH value of 4.7, temperature of 80 degrees C, SDS content of 1.2 mg/L, and Al2O3 content of 3 g/L. Under these parameters, the prepared coating achieved an aluminum content of 7.859 %, which is 2-3 times higher than the formulations obtained from orthogonal experiments. The coating hardness increased to 1577 HV, three times that of the base Ni-P coating, with a thickness of 52.46 mu m, and exhibited excellent interfacial adhesion. In the friction wear test, the wear rate was reduced by 40.7 % and 30 % compared to GCr15 steel and the best formulation from orthogonal experiments, respectively.

关键词： Nickel-phosphorus composite plating BP neural network pso algorithm Wear resistance

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Self-tuning vibration reduction study of magnetic fluid rolling-ball damper based on pso-FUZZY-PID

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第1期238卷 29-47页

作者： Yang, Wenrong Zhang, Dalei Zhang, Yumeng Sun, Changxing Hou, Lizheng Wei, Dejie Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300130 Peoples R China

For towering structures under complex time-varying excitation, the tuned magnetic fluid rolling-ball dampers (TMFRBD) cannot achieve self-tuning damping. In this paper, considering the operating characteristics of the TMFRBD, a damper self-tuning control algorithm, namely particle swarm optimization (pso)-FUZZY-PID, is proposed for real-time adjustment of the intrinsic frequency of TMFRBD, which can greatly improve the damping effect, namely self-tuning magnetic fluid rolling-ball dampers (STMFRBD). Firstly, the equivalent dynamics model of the damping system is established, deriving the structural dynamic amplitude response as a function of the excitation frequency to structural frequency ratio. Different frequency tracking schemes of the damper are compared and analyzed. Then magnetic field simulations and magnetic fluid magnetic property measurements are performed for the dampers, respectively. Secondly, the self-tuning pso-FUZZY-PID control algorithm is designed to improve the damping performance of STMFRBD under time-varying excitation by optimizing factors of the FUZZY-PID controller with pso. To confirm the effectiveness of the damper self-tuning control algorithm, the frequency tracking of dampers with different control algorithms under time-varying excitation is compared. The results show that the damper's frequency can be tuned quickly and accurately tracking excitation frequency by pso-FUZZY-PID. Finally, the simulation and experimental results of STMFRBD and passive mass damper (PMD) under different loads are analyzed, and the structural amplitude response is analyzed using the Hilbert-Huang transform (HHT). In addition, the damping of STMFBD and TMFBD under different loads is compared. The results show that STMFRBD has better vibration-damping performance than PMD and TMFRBD.

关键词： Magnetic fluid rolling-ball damper pso algorithm time-varying excitation FUZZY-PID control frequency tracking scheme

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PM2.5 Measuring Method Using RBF Neural Network Combined With pso algorithm

PM2.5 Measuring Method Using RBF Neural Network Combined Wit...

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第27届中国控制与决策会议

作者： XU Lin GUAN Tian-yi LIYan-nong ZHENG Wen-jing GUO Jing-yi College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479970186

Considering the disadvantage of PM2.5 detection devices,there are many problems such as the low automation,poor test repeatability,and dielectric material loss *** paper presents a new PM2.5 detection system using laser diffraction technique based Fraunhefor diffraction *** Radial Basis Function(RBF) neural network with the inputs of multiple laser diffraction signals is used to be the micro particles calculating model to improve the detecting *** tackle the problems in the training algorithms,the Particle Swarm Optimization(pso)algorithm is employed to optimize the key parameters of the RBF neural network(RBFNN).The simulation and experiment results show that the new PM2.5 detection system satisfies the detection requirements with the high calculating precision,and effectively overcomes the problems in the conventional detection system.

关键词： PM25 detection Laser diffraction RBF neural network pso algorithm

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Enhancement neural control scheme performance using pso adaptive rate: Experimentation on a transesterification reactor

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JOURNAL OF VIBRATION AND CONTROL 2023年第15-16期29卷 3769-3782页

作者： Yassin, Farhat Ali, Zribi Asma, Atig Ridha, Ben Abdennour Univ Gabes Natl Engn Sch Gabes Res Lab Numer Control Ind Proc Gabes Tunisia

An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and controller with decoupled adaptive rates. Constraints on the adaptive rate are derived from the Lyapunov stability method. Particle Swarm Optimization is proposed as a mechanism to optimize the adaptive rate of the NC to improve the closed-loop performances. The advantages of the proposed new control algorithm are as follows: (1) online optimal choice of adaptive rate, which reduces the effort for searching an adequate neural controller adaptive rate when considering the conventional methods and (2) ensuring stability, faster convergence, disturbance rejection, and good tracking. The efficiency of the proposed pso adaptive rate is demonstrated with numerical control of SISO nonlinear system. The obtained results prove the efficiency of the proposed NC compared to those obtained with existing methods. An application of the developed approach on a semi-batch reactor is presented to validate simulations results.

关键词： Nonlinear systems adaptive neural control lyapunov stability pso algorithm chemical reactor

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