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检索条件"主题词=Particle Swarm optimization algorithm"

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Thermal Chips Layout Method in MCM Based on an Improved particle swarm algorithm 31

Thermal Chips Layout Method in MCM Based on an Improved Part...

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31st Chinese Control And Decision Conference (CCDC)

作者： Ning, Ye Jie, Yang Ye, Tao Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

The emergence of Multichip Module(MCM) means that the development of component technology is greatly improved in number, speed and performance. Research on thermal layout optimization methods for MCM can promote its reliability, this paper established a numerical relationship model between junction temperature and coordinates of each chip. and uses ANSYS to simulate and analyze the temperature profile of the optimization results. This paper compares several typical optimization schemes based on (W) and presents the improved particle swarm algorithm. Experimental results show that improved particle swarm algorithm can improve inertia weight and maximum speed effectively, and also can achieve lower chip junction temperature in chip layout.

关键词： Multi-chip Module(MCM) particle swarm optimization algorithm Thermal Layout optimization Inertia Weight

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Soft Sensor Measurement Research on Resolution Ratio of Cement Kiln Tail Based on IPSO-SVR

Soft Sensor Measurement Research on Resolution Ratio of Ceme...

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Chinese Automation Congress (CAC)

作者： Xu, Ting Leng, Miao Guo, Pan Gong, Xin Li, Xuejun Zhang, Liying Northeast Normal Univ Sch Informat Technol & Engn Coll Humanities & Sci Changchun Peoples R China State Grid Jilin Elect Power Co Ltd Liaoyuan Power Supply Co Power Dispatch Control Ctr Liaoyuan Peoples R China

ISBN: (纸本)9781728140940

In order to realize the real-time on-line detection on resolution ratio of cement kiln tail, by means of the advantages from soft sensor measurement technology in which process parameters cannot be directly measured at present, a support vector regression (SVR) optimized by the improved particle swarm optimization (IPSO) algorithm is proposed. Based on the idea of adaptive weight, this algorithm overcomes the shortcomings of PSO which is prone to show premature convergence and poor local search ability, and improves its global search ability and local improvement ability. The parameters of SVR machine are optimized, and then soft sensor measurement model of resolution ratio of cement kiln tail is established. The simulation is compared with those based on the cross validation method and PSO, the results show that the IPSO-SVR algorithm has better ability of modeling, prediction and generalization, and the average relative error of prediction is 0.75%, so it can be further applied to the product real-time on-line detection on large-scale industries such as cement production.

关键词： soft sensor measurement particle swarm optimization algorithm support vector regression machine

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optimization of Vehicle Management for One-Way Carsharing Systems Based on Time Windows of Users 8

Optimization of Vehicle Management for One-Way Carsharing Sy...

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8th International Conference on Industrial Technology and Management (ICITM)

作者： Shen, Ling Tao, Fengming Chongqing Univ Coll Mech Engn Chongqing Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing Peoples R China

ISBN: (纸本)9781728132686

In order to reduce the operating costs of car-sharing companies and improve the satisfaction of users, a one-way carsharing systems optimization model which considers the time windows requirements of users is constructed with minimum total costs including scheduling costs and penalty costs. Then, an improved particle swarm optimization algorithm with tabu search is proposed to solve the model. Numerous experiments are performed to obtain the optimal solution. Finally, based on experimental results, some practical proposals for one-way carsharing systems are provided for the carsharing companies.

关键词： vehicle scheduling carsharing time windows particle swarm optimization algorithm tabu search

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Bidding Strategy of Thermal Units Participating in Real-Time Depth Peak Load Regulation Ancillary Service Market based on Stackelberg Game

Bidding Strategy of Thermal Units Participating in Real-Time...

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Asia Power and Energy Engineering Conference (APEEC)

作者： Yang, Jianlin Guo, Mingxing Fei, Fei Gong, Kai Wang, Xu Jiang, Chuanwen State Grid Corp China Shanghai Municipal Elect Power Co Shanghai 200122 Peoples R China Shanghai Jiao Tong Univ Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781728115900

With the development of the peak load regulation ancillary service market and the improvement of the rules, the role of thermal power unit changed from the passive participant in peak load regulation for duty to the active bidder. The power grid dispatching agency called the depth peak load regulation (DPLR) resources on demand according to the price. In order to research the optimal bidding strategy of thermal power units in the depth peak load regulation ancillary service market, a bi-level model of multiple thermal power manufacturers and single market dispatching institution is established based on stackelberg game. The upper level is the bidding strategy of thermal power manufacturers, including the income and cost of thermal power manufacturers. The lower level is the optimal scheduling scheme of the market dispatching institution. The nonlinear bi-level model is solved by the particle swarm optimization algorithm. The cases show the importance and validity of the proposed model

关键词： bi-level optimal model stackelberg game particle swarm optimization algorithm depth peak load regulation

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Design of PI^λD^δ-Heart Rate Controllers for Cardiac Pacemaker 19

Design of PIλDδ-Heart Rate Controlle...

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19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Momani, Shaker Batiha, Iqbal M. El-Khazali, Reyad Ajman Univ Coll Humanities & Sci Dept Math & Sci Ajman U Arab Emirates Univ Jordan Fac Sci Dept Math Amman 11942 Jordan Khalifa Univ ECCE Dept Abu Dhabi 127788 U Arab Emirates

ISBN: (纸本)9781728153414

An arrhythmia is any disorder of your heart rate or rhythm. It means that the heartbeats too quickly, too slowly, or with an irregular pattern. Most arrhythmias result from problems in the electrical system of the heart. A cardiac pacemaker is a medical device that invented for those reasons. It helps the suffering patient to control abnormal heart rhythms. Several proportional-integral-derivative (PID) controllers have been proposed for improving the performance of such device, but it was shown that each of them has some limitations in facing the dynamic challenge of regulating heart rate. In this paper, we propose a robust fractional-order PID ((PID delta)-D-lambda) controller to improve that performance. This controller has been designed based on particle swarm optimization (PSO) algorithm via El-Khazali's approach. To illustrate the proposed design method, the objective function of the model under consideration is presented as well as the results of using the (PID delta)-D-lambda-controller are compared with the results of using the conventional PID-controller to minimize several error functions;ITAE, IAE, ISE and ITSE. These results of such comparisons that are related to step response specifications, allow us to see the effectiveness of the best controller. Among of all these performance indices, the ITAE in case of the (PID delta)-D-lambda-controller, shows more capability than others in providing closed-loop response with minimal overshoot, fast rise time and fast settling time. Further, to illustrate the influence and the efficiency of the proposed design method, the PSO-(PID delta)-D-lambda-controller via El-Khazali's approach is compared with that of the same using two well-known approximations for s(+/-alpha);i.e., the Continued Fractional Expansion (CFE) and Oustaloup's approximations. The main results of this work are verified via numerical simulations.

关键词： cardiac pacemaker (PID delta)-D-lambda-controller particle swarm optimization algorithm Laplacian operator El-Khazali's approach

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A Survey of Artificial Intelligence algorithm in Power System Applications 3

A Survey of Artificial Intelligence Algorithm in Power Syste...

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3rd IEEE International Electrical and Energy Conference (CIEEC)

作者： Cai, Hongwei Lu, Xiaodan Du, Ting Wang, Yixian Xia, Shiwei Zhang, Dongying State Grid Beijing Elect Power Co Beijing Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728116754

With the power network expansion and the grid-connection of intermittent renewable energy, more and more non-convex optimization problems emerge in power system, which cannot be effectively solved by the derivative based traditional methods. As a powerful branch of optimization techniques, artificial intelligence method can well address these non-convex problems, providing a promising solution for power system engineering. In this paper, five typical artificial intelligence algorithms including the particle swarm optimization algorithm, Tabu Search, Genetic algorithm, Simulated Annealing and Artificial Neural Network are investigated for their advantages and disadvantages, and the main application in power system for these artificial intelligence algorithms arc also discussed.

关键词： Power System Artificial Intelligent algorithms particle swarm optimization algorithm Tabu Search Genetic algorithm Simulated Annealing Artificial Neural Network

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Multi-objective reactive power optimization in power system based on improved PSO-OSA algorithm 14

Multi-objective reactive power optimization in power system ...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Song Bo Cao Yujie Zhang Xiang Wang Yelin Li Ning Xian Univ Technol Inst Water Resources & Hydroelect Engn Xian Shaanxi Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian Shaanxi Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China Xian Kenuo Network & Informat Secur Technol Inst Xian Shaanxi Peoples R China

ISBN: (纸本)9781538694909

With the rapid development of Chinese economy, the power industry is playing a more and more important role in the development of Chinese economy. The optimal reactive power in the power system plays an important role in the voltage stability of the power system and reducing the loss of active power. The calculation results of particle swarm optimization algorithm, oriented search algorithm and PSO - OSA hybrid algorithm in reactive power optimization of power system is compared, and the performance of the proposed method has been tested on the IEEE 30-bus systems in this paper. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the optimal reactive power problem.

关键词： Optimal reactive power Multi-objective probelem particle swarm optimization algorithm Oriented search algorithm

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Virtual Force Senor Based on PSO-BP Neural Network for Legged Robots in Planetary Exploration 12th

Virtual Force Senor Based on PSO-BP Neural Network for Legge...

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12th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Wang, Chu Wu, Shuang Chen, Lei Liu, Bin Wei, Qingqing Wang, Yaobing Beijing Inst Spacecraft Syst Engn Beijing Key Lab Intelligent Space Robot Syst Tech Beijing 100094 Peoples R China

ISBN: (纸本)9783030275389;9783030275372

The foot force of the legged robot plays a decisive role in the balance of the fuselage. Especially when walking on the irregular road surface and the less rigid road surface, the change of the foot end support force will change the attitude of the robot body, which will affect the stability. In addition, the change of foot force is also closely related to the flexibility of the movement of the leg of the robot. When the movement of the foot end has a transition from free space to constrained space, only the position control will not meet the requirements of the leg for the flexibility of the movement. This paper addresses the design of virtual sensors for terrain adaptation developed with the aims of simplifying the hardware of the legged robot or increasing the reliability of the sensorial information available. The virtual force sensor (VFS) is developed based on particle swarm optimization (PSO) BP neural network and can estimate the forces exerted by the feet from data extracted from joint-position, joint-velocity, and joint-torque, which are mandatory in all robotic systems. The force estimates are used to detect foot/ground contact. Several simulations carried out with the hexapod robot are reported to prove the efficacy of this method. This method simplifies the hardware of the robot, reduces design, construction and maintenance costs while enhancing the robustness of the robot and the reliability of its behavior.

关键词： Virtual force sensor particle swarm optimization algorithm BP neural network Legged robot

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Dynamic Responses of PI^λ, PD^δ, and PI^λD^δ Controllers for Prosthetic Hand Model Using PSO algorithm 19

Dynamic Responses of PIλ, PDδ, and P...

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19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Batiba, Iqbal M. El-Khazali, Reyad Momani, Shaker Univ Jordan Fac Sci Dept Math Amman 11942 Jordan Khalifa Univ ECCE Dept Abu Dhabi 127788 U Arab Emirates Ajman Univ Coll Humanities & Sci Dept Math & Sci Ajman U Arab Emirates

ISBN: (纸本)9781728153414

Prosthetic hand is a device designed to help those with full or partial hand loss retain the function and appearance of a regular hand. It has been shown that such device needs to be enhanced in the control capabilities and functional devising. Thus, a robust design is required to enhance the performance of prosthetic hand model, and also improve existing capabilities to the optimum level. The objective of this paper is to design and analyze the Fractional-order Proportional Integral (PI lambda), Fractional-order Proportional Derivative (PD delta), and Fractional-order Proportional Integral Derivative ((PID delta)-D-lambda) controllers for the model under consideration, in order to realize accurate control with high performance. The effect of each controller on the dynamics of a closed-loop system has been discussed and analyzed. Tuning parameters for each controller has been done using particle swarm optimization (PSO) algorithm via El-Khazali's approach. Different simulated comparisons reveal that the performance of (PID delta)-D-lambda-controller is outperform than other controllers in terms of rise time, settling time and overshoot.

关键词： prosthetic hand model (PID delta)-D-lambda-controller particle swarm optimization algorithm Laplacian operator El-Khazali's approach

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying Cheetah optimization algorithm

Non-uniformly spaced linear antenna array design by means of...

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15th Biennial International Workshop on optimization and Inverse Problems in Electromagnetism (OIPE)

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired Cheetah metaheuristic. The main aim of this paper is to introduce the Cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array Cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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