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检索条件"主题词=particle swarm algorithm"

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A Novel EEG Classification Technique Based on particle swarm Optimization for Hand and Finger Movements 5th

A Novel EEG Classification Technique Based on Particle Swarm...

引用

5th International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Wafeek, Nourhan Mubarak, Roaa, I Elbably, Mohamed E. Helwan Univ Cairo Egypt

ISBN: (纸本)9783030311292;9783030311285

Electroencephalogram (EEG) has gained much attention from researchers recently. EEG classification has many applications such as: classifying brain disorders, helping paralyzed people to control a machine by their own imagery mental tasks and controlling a robot or a remote system with both imagery and actually mental tasks. This paper aims to classify arm and finger movements acquired through EEG signals. The EEG signals have been transformed to frequency domain using discrete wavelet transform (DWT) as a feature extractor. These extracted features are then feed into a novel particle swarm classifier to classify the different movements of arm and fingers. The experimental results showed that this new algorithm gives accuracy of 95% with minimum time delay, which is an essential requirement for all biomedical applications. This research is considered the first step towards implementing an automatic system (surgical robot) that can be used in Telesurgery.

关键词： EEG BCI BMI Wavelet transform particle swarm algorithm

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Study on Automatic Test Generation of Digital Circuits Using particle swarm Optimization

Study on Automatic Test Generation of Digital Circuits Using...

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10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Gu Yuan-liang Xu Wen-bo Jiangnan Univ IoT Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9780769544151

The development of digital integrated circuit has put forward urgent demands for test technology. Test technology has become a bottleneck in the application of LSI/VLSI. Especially for sequential circuits, it is still a problem which is not resolved completely in theory. By making use of the structure information of circuits, a method of automatic test generation for sequential circuits based on particle swarm optimization is presented, which is performed by two steps, initialization and fault detection. Experimental results show that the approach can achieve high fault coverage, and CPU times needed for test generations are very short, which shows that it is a method deserving research.

关键词： Automatic Test Generation Sequential Circuits particle swarm algorithm

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A Novel Multi-Objective particle swarm Optimization based on Dynamic Crowding Distance

A Novel Multi-Objective Particle Swarm Optimization based on...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Liu, Liqin Zhang, Xueliang Xie, Liming Du, Juan Lanzhou Univ Technol Coll Mech Elect Engn 85 Langongping Lanzhou 730050 Gansu Peoples R China Taiyuan Univ Sci & Technol Coll Mech Elect Engn Taiyuan 030024 Shanxi Peoples R China

ISBN: (纸本)9781424447541

In this article, a multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed, in which the definition of DCD was based on the degree of difference between the crowding distances on different objectives The proposed approach computed individual's DCD dynamically during the process of population maintenance to ensure sufficient diversity amongst the solutions of the non-dominated fronts Introducing the improved quick sorting to reduce the time for computation, both the dynamic inertia weight and acceleration coefficients are used in the algorithm to explore the search space more efficiently Experiments on well known and widely used test problems are performed, aiming at investigating the convergence and solution diversity of DCD-MOPSO The obtained results are compared with MOPSO and NSGA-II, yielding the superiority of DCD-MOPSO

关键词： particle swarm algorithm multi-objective optimization dynamic crowding distance Pareto set

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Multi-objective particle swarm Optimisation for Phase Specific Cancer Drug Scheduling 1

引用

1st International Conference on Computational Systems Biology and Bioinformatics

作者： Alam, Mohammad S. Algoul, Saleh Hossain, M. Alamgir Majumder, M. A. Azim Univ Bradford Bradford BD7 1DP W Yorkshire England

ISBN: (数字)9783642167508

ISBN: (纸本)9783642167492

An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient's body and MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72% and 60% respectively;maximum reduction with phase-specific model compared to reported work available so far.

关键词： Phase specific scheduling Cancer chemotherapy Cell compartment Feedback control Multi-objective optimisation particle swarm algorithm

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The gregarious particle swarm optimizer (G-PSO) 06

The gregarious particle swarm optimizer (G-PSO)

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8th Annual Genetic and Evolutionary Computation Conference

作者： Pasupuleti, Srinivas Battiti, Roberto Univ Trento Dept Comp Sci & Telecommun Trento Italy

ISBN: (纸本)9781595931863

This paper presents a gregarious particle swarm optimization algorithm (G-PSO) in which the particles explore the search space by aggressively scouting the local minima with the help of only social knowledge. To avoid premature convergence of the swarm, the particles are re-initialized with a random velocity when stuck at a local minimum. Furthermore, G-PSO adopts a "reactive" determination of the step size, based on feedback from the last iterations. This is in contrast to the basic particle swarm algorithm, in which the particles explore the search space by using both the individual "cognitive" component and the "social" knowledge and no feedback is used for the self-tuning of algorithm parameters. The novel scheme presented, besides generally improving the average optimal values found, reduces the computation effort.

关键词： particle swarm algorithm repeated affine shaker differential evolution

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The Research and Application of particle swarm Optimization in Parameter Tuning on PID Controller

The Research and Application of Particle Swarm Optimization ...

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Tang, Yulan Chen, Jianhui Wuxi City Coll Dept Elect & Informat Engn Wuxi 214151 Jiangsu Peoples R China Wu Xi Inst Technol Coll Elect & Informat Technol Wuxi 214121 Peoples R China

ISBN: (纸本)9783037858882

A design method of PM controller based on particle swarm algorithm is proposed to solve the difficult problems of parameter tuning on PID controller in automatic control system. And the specific experimental structure is also given. The transfer function of DC servo generator was found with identification of system parameters, and the PID parameters were searched by particle swarm algorithm. MATLAB simulation was used to demonstrate the feasibility and advantages of this approach. The simulation result was compared to the result of searching PID parameters based on genetic algorithm, and it is show that the seeking time to tune the PID parameters by using the particle swarm algorithm is faster than by using the genetic algorithm method.

关键词： Genetic algorithm PID control Parameters Tuning particle swarm algorithm

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Speed Tracking Control of Unmanned Patrol Vehicle based on particle swarm Optimization 33

Speed Tracking Control of Unmanned Patrol Vehicle based on P...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Zheng, Jinlong Li, Meng Yang, Zeyun Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China Shandong Longyi Aviat Technol Co Ltd Jining 272000 Peoples R China

ISBN: (纸本)9781665440899

Unmanned patrol vehicle is a typical mobile robot, which is an important equipment to realize intelligent patrol in outdoor environment such as industrial and mining enterprises, judicial prisons, docks and warehouses. In order to solve the problem of long speed regulation time and poor stability of inspection unmanned vehicle in complex environment, this paper designs a fuzzy PID speed control method based on particle swarm optimization algorithm. On the basis of conventional fuzzy PID, combined with particle swarm optimization algorithm, the membership function of fuzzy controller is optimized to reduce the dependence of fuzzy control on expert experience. Simulation results show that this method can reduce the speed regulation time and improve the stability of speed control.

关键词： particle swarm algorithm Fuzzy PID control Unmanned patrol vehicle Parameter setting

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Research on Improvement of particle swarm Optimization

Research on Improvement of Particle Swarm Optimization

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Chang, Chunguang Wu, Xi Shenyang Jianzhu Univ Sch Management Shenyang 110168 Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Although the particle swarm optimization algorithm has simple principle, few parameters and easy implementation, the particle swarm optimization algorithm is easy to fall into local optimum on multi-mode function and the local search ability is relatively weak. In this paper, the improvement of these two defects is carried out. The particle motion formula with learning model is added, and the generation strategy of a guided vector is added to improve the particle swarm optimization algorithm. The improved algorithm has a two-layer structure, and finally the research direction is prospected.

关键词： particle swarm algorithm Two-layer structure Improvement

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A fuzzy PI controller for piezoelectric fast mirror based on particle swarm optimization 16

A fuzzy PI controller for piezoelectric fast mirror based on...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Dong, Yutong Li, Cong Liu, Lei Yang, Hongjie Chen, Te Cao, Guixing Northwestern Polytech Univ Shaanxi Aerosp Flight Vehicle Design Key Lab Xian Peoples R China China Acad Space Technol Inst Telecommun & Nav Satellites Beijing Peoples R China

ISBN: (纸本)9781665422482

In this paper, the hysteresis model and electric-mechanical dynamic model in series are presented to model the complex dynamic behavior of piezoelectric fast steering mirror. To realize its precise control, a compound controller is designed by combining the feedforward control based on Bouc-Wen inverse model and fuzzy PI control. Then, the parameters of fuzzy PI are adjusted by particle swarm optimization algorithm. Finally, the simulation and experimental results verify the effectiveness of the proposed control method compared with the conventional PI control.

关键词： piezoelectric fast steering mirror hysteresis fuzzy PI particle swarm algorithm

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Application of support vector machine and particle swarm optimization in Micro Near Infrared Spectrometer 8

Application of support vector machine and particle swarm opt...

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8th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT) - Design, Manufacturing, and Testing of Micro- and Nano-Optical Devices and Systems;and Smart Structures and Materials

作者： Xiong, Yuhong Liu, Yunxiang Shu, Minglei Shanghai Inst Technol Coll Comp Sci & Technol Shanghai 201418 Peoples R China

ISBN: (纸本)9781628419207

In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.

关键词： Micro near infrared spectrometer spectral analysis support vector machine particle swarm algorithm

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