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Fuzzy Fractional PID Controller Tuned through a pso algorithm 1

11th Portuguese Conference on Automatic Control

作者： Jesus, Isabel S. Barbosa, Ramiro S. Polytech Porto ISEP IPP GECAD Knowledge Engn & Decis Support Res Ctr Dept Elect Engn Inst Engn Oporto Portugal

ISBN: (数字)9783319103808

ISBN: (纸本)9783319103792

The differentiation of non-integer order has its origin in the seventeen century, but only in the last two decades appeared the first applications in the area of control theory. In this paper we consider the development of an optimal fuzzy fractional PD beta+I controller in which the parameters are tuned by a particle swarm optimization (pso) algorithm. Several simulations are presented assessing the performance of the proposed controller.

关键词： fractional calculus fuzzy fractional PID control systems pso algorithm optimization

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Optimizing Self-organizing method in traffic lights with pso algorithm in a single junction modeled with a cellular automaton 1

Optimizing Self-organizing method in traffic lights with PSO...

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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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Improved Regularization based Blind Image Deconvolution using pso algorithm for PMMW Images Application 9

Improved Regularization based Blind Image Deconvolution usin...

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9th Iranian Conference on Machine Vision and Image Processing (MVIP)

作者： Mansoori, M. A. Mosavi, M. R. Bisjerdi, M. H. Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

ISBN: (纸本)9781467385398

Blind deconvolution algorithms in image processing estimate the original image and the true Point Spread Function (PSF) simultaneously with prior information about the PSF or the original image. This is an ill- posed problem and requires regularization to be solved. In addition to the type of regularization functions, the value of regularization parameters can drastically affect the output result. In this paper, we propose a Particle Swarm Optimization (pso) algorithm for selecting optimum values of regularization parameters in blind image deconvolution. The algorithm has been tested on standard images and then compared with non- improved ones, in terms of three standard metrics. A real Passive Millimeter Wave (PMMW) image blurred by an unknown PSF is also used in this algorithm to obtain a sharp deblurred image with an estimate of the PSF. Simulation results show that the proposed method improves the quality of the estimated PSF and the deblurred image.

关键词： Blind deconvolution pso algorithm PSF estimation PMMW images regularization parameter

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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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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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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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Improve customer churn prediction through the proposed PCA-pso-K means algorithm in the communication industry (vol 79, pg 6871, 2023)

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JOURNAL OF SUPERCOMPUTING 2023年第13期79卷 15212-15212页

作者： Sadeghi, Maryam Dehkordi, Mohammad Naderi Barekatain, Behrang Khani, Naser Islamic Azad Univ Dept Management Najafabad Branch Najafabad Iran Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Customer churn prediction is one of the areas in Customer Relationship Management that differentiates loyal customers from factors that have a negative impact on business growth. Hence, various machine learning-based methods have been developed by researchers to accurately predict customer churn. However, high dimensionality and low prediction accuracy are problems in identifying averse customers. This paper presents a new system called PCA-pso-K Means algorithm, which combines three algorithms: principal component analysis (PCA) for data set feature reduction, K Means algorithm for classification, and particle swarm optimization (pso) algorithm to optimize K Means in providing initial centroids. The experimental results in the data set of one of the fixed internet providers in Isfahan Province show the improvement of the accuracy of customer churn prediction. The proposed system has an accuracy of 99.77%, a sensitivity of 75%, a specificity of 99.81% and a correlation coefficient of 0.443 ± 0.271. Found.

关键词： CRM Customer churn prediction Data mining Principal component analysis K Means algorithm pso algorithm

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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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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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State of charge estimation of lithium-ion batteries based on pso-TCN-Attention neural network

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JOURNAL OF ENERGY STORAGE 2024年 84卷

作者： Li, Feng Zuo, Wei Zhou, Kun Li, Qingqing Huang, Yuhan Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Peoples R China Univ Technol Sydney Ctr Green Technol Sch Civil & Environm Engn Ultimo NSW 2007 Australia Univ Oxford Dept Engn Sci Oxford OX1 3PJ England Wuhan Univ Sci & Technol Natl Prov Joint Engn Res Ctr High Temp Mat & Linin Wuhan 430081 Peoples R China

Lithium -ion batteries are acted as energy storage devices and widely used in many fields, such as mobile, electric vehicles, and renewable energy sources, etc. However, their reliability, performance and safety are limited by state of charge (SOC) estimation of Lithium -ion batteries. In order to address this issue, a pso-TCN-Attention network model is proposed in this work. The particle swarm optimization (pso) algorithm is utilized to optimize the structural parameters of temporal convolutional network (TCN), enabling the model automatically learn and adapt the characteristics of lithium -ion batteries under different temperature conditions. Then, the attention mechanism allows the network adaptively focus on key time steps, enhancing the capture of time dependency in the SOC estimation from the Lithium -ion battery dataset, and further improving the accuracy and robustness of the model. Moreover, as the dataset used for SOC prediction consists of battery data from all dynamic operating conditions at various temperatures, the model is validated against LSTM and TCN networks. Results demonstrate that the SOC estimation of pso-TCN-Attention network model is the most optimal, whose RMSE and MARE is less than 1 % and 5.75 %, respectively, and R2 coefficient of determination exceeds 99.88 %.

关键词： Lithium -ion battery State of charge estimation Attention mechanisms pso algorithm pso-TCN-Attention network

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