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检索条件"主题词=particle swarm algorithm"

共 736 条记录，以下是601-610 订阅

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particle swarm OPTIMIZATION algorithmBASED FUZZY NEURAL NETWORK

PARTICLE SWARM OPTIMIZATION ALGORITHMBASED FUZZY NEURAL NETW...

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第11届国际模糊系统协会世界大会

作者： Dong Hwa Kim Jae Hoon Cho Dept.of Instrumentation and Control Eng. Hanbat National University 16-1 San Duckmyong-Dong Yuseong-Gu Daejon City Korea 305-719 Dept.of Instrumentation and Control Eng. Hanbat National University 16-1 San Duckmyong-Dong Yuseong-Gu Daejon City Korea 305-719

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

关键词： Fuzzy neural network particle swarm algorithm Multiobjecdve control Optimization.

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The causes analysis of Ischemic Stroke transformation into Hemorrhagic Stroke using PLS (partial least square)-GA and swarm algorithm

The causes analysis of Ischemic Stroke transformation into H...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Shih, Chihhsiong Chang, You-Wei Chu, William Cheng-Chung Dept Comp Sci Taichung Taiwan

ISBN: (纸本)9781728126074

Ischemic stroke has been known to convert to Hemorrhagic stroke with ease. It is crucial to identify high risk patients with relevant diseases. This study uses the Taiwan National Health Insurance database to collate and analyze the relevant diseases leading to Hemorrhagic from Ischemic stroke. We propose several novel machine learning based algorithms and indexing methods for disease transformation prediction and accuracy enhancements. They are a modified swarm algorithm, partial least square(PLS) algorithm and genetic algorithms. From the petition application files accumulated from 2006 to 2013 within the National Health Insurance Database, 8,483 patients with ischemic stroke is collected. Among them, the 1,145 patients with both ischemic stroke and hemorrhagic stroke were screened according to the ICD-9-CM diagnostic code. The disease history of each patient is vectorized and stacked into a matrix for analysis. The PLS/GA process is then applied on the disease history matrix, trying to filter out the candidate diseases leading to such transformation. A total of 750 diseases were found to be associated with ischemic stroke and hemorrhagic stroke through the PLS/GA process. A modified PSO algorithm is developed to further weight these selected diseases. A quartile rule is then applied to filter these weighted diseases to ten most influential diseases which are dizziness, constipation, chronic renal failure, hypertension, diabetes, hyperlipidemia, anxiety, muscle pain, prostatic hypertrophy, etc. In addition to normally known diseases to such conversion in the literature such as hypertension and diabetes, we also discovered more potential diseases. The initial analysis accuracy of our proposed methods reaches 86% on average, while that of the traditional Neural Network algorithm utilizing the same training data was only 49.5%.

关键词： Partial Least Square algorithm Genetic algorithm particle swarm algorithm ischemic stroke hemorrhagic stroke

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On the Global Optimum Motion Planning for Dynamic Coupling Robotic Manipulators Using particle swarm Optimization Technique

On the Global Optimum Motion Planning for Dynamic Coupling R...

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2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

作者： Zhijie Wen Jun Luo Zhijun Li Department of Automation Shanghai Jiao Tong UniversityShanghaiChina School of Mechatronics Engineering & Automation Shanghai UniversityShanghai

ISBN: (纸本)9781424447749

In this paper, global optimization approach using particle swarm optimization technique for motion planning of dynamic coupling robotic manipulators, involving the optimization of dynamic performance criteria, has been shown to exhibit certain advantages compared with local optimizers. In this approach, the joint torques of the robotic manipulator are approximated by parameterized B-spline functions, and then the particle swarm optimization algorithm is used to determine the optimal parameters. The optimal motion is thus obtained which satisfies the dynamic constraints condition. By performing numerical simulations, the effectiveness of the proposed motion planning by particle swarm optimization technique is verified.

关键词： particle swarm optimization motion planning Robot Manipulator Manipulators global optimum particle swarm algorithm constraint condition

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A Novel particle swarm Optimization with Random Parameters

A Novel Particle Swarm Optimization with Random Parameters

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2012 2nd lnternational Conference on Materials Engineering for Advanced Technologies (ICMEAT 2012)

作者： Shaorong Huang Department of Information Management Guangdong Justice Police Vocational College

To improve the performance of standard particle swarm optimization algorithm that is easily trapped in local optimum,based on analyzing and comparing with all kinds of algorithm parameter settings strategy,this paper proposed a novel particle swarm optimization algorithm which the inertia weight(ω) and acceleration coefficients(c1 and c2) are generated as random numbers within a certain range in each iteration *** experimental results show that the new method is valid with a high precision and a fast convergence rate.

关键词： particle swarm optimization (PSO) Inertia weight Acceleration coefficients Random Parameters speedup factor particle swarm optimization Iterative Process local optimum random numbers particle swarm algorithm

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Research on Obstacle Avoidance Path Planning for Unmanned Aerial Vehicle Formation in Urban Environment 14

Research on Obstacle Avoidance Path Planning for Unmanned Ae...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Zhang, Tianyao Guo, Lixin Wei, Yiwen Xidian Univ Sch Phys Xian Peoples R China

ISBN: (纸本)9798350353129;9798350353136

The application of Unmanned Aerial Vehicle (UAV) in military, civil fields is becoming increasingly prevalent. Due to the complexity of the environment and the diversity of missions, current UAV technology is evolving towards the concept of swarm concept. In this paper, we introduce the environmental factors of the simulation and then adopt the elevation data of the urban model. Subsequently, the Rodrigues' rotation formula is introduced to facilitate the rotation of the UAV formation, combined with the translational coordinates of the path planning, in order to enable the transportation of the UAV formation within the three-dimensional environment. The particle swarm algorithm is highlighted as a key component in the planning of paths for point target formations in urban environment. The algorithm enables the control of distances between targets, the planned paths can be applied in the UAV swarm after the expansion process of the urban model. The above parts are finally integrated to realize the UAV formation's obstacle avoidance trajectory in urban environment.

关键词： UAV Urban model Rodrigues' rotation formula particle swarm algorithm

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Real-time Evaluation Model Construction of GIS Conductor Thermal State Based on PSO-BP Neural Network 3

Real-time Evaluation Model Construction of GIS Conductor The...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Jiao, Kerui Shen, Xiaojun Tongji University Department of Electrical Engineering Shanghai China

ISBN: (纸本)9798350382426

The thermal state of the conductors inside the GIS is an important indicator to assess its safety, but due to the closed nature of the GIS, it is often difficult to achieve a relatively fast monitoring. At present, multi-physics field simulation with the help of finite element method can realize the rapid monitoring of the thermal condition inside GIS, but some of its input parameters are difficult to obtain, and often can only' explore the causes from the results'. Meanwhile, it is difficult to guarantee the real-time performance. In this paper, we propose and construct a real-time evaluation model of conductor heating condition based on multi-physics field simulation and a BP neural network optimized by particle swarm algorithm. With the multi-physics field simulation model, parameters such as current, conductor resistance, gas pressure, and shell temperature rise are extracted as inputs, and contact resistance and conductor temperature rise are used as outputs to train the PSO- BP neural network to build the inverse model of GIS conductor heating state. The inversion system is highly accurate and real-time, reducing the input parameters that are difficult to obtain, and effectively realizing the improvement and enhancement from 'exploring the causes from the results' to 'deriving the results from the causes'. © 2023 IEEE.

关键词： GIS conductor particle swarm algorithm PSO-BP neural network real-time evaluation thermal state

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Time-optimal trajectory planning of a six-degree-of-freedom robotic arm based on improved PSO algorithm

Time-optimal trajectory planning of a six-degree-of-freedom ...

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2024 International Conference on Intelligent Robotics and Automatic Control

作者： Yang, Ruijun Sun, Qingyu Xu, Bin Ji, Shoucheng Shanghai Inst Technol Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9798350389814;9798350389807

To increase the working efficiency of the robotic arm and its stability, a 3-5-3 polynomial interpolation trajectory optimization algorithm based on the improved particle swarm algorithm is proposed for the time-optimal planning problem of a 6-DOF robotic arm. The improved particle swarm optimization algorithm improves the algorithm's global search ability and local search ability by nonlinearly and dynamically adjusting the size of inertia weights and learning factors. The improved particle swarm algorithm is used to optimize the robotic arm motion trajectory in the shortest time under the constraints such as speed. The research results show that the optimized particle swarm algorithm has higher optimization accuracy and faster convergence speed, and the overall running time of the robotic arm optimized by the improved algorithm is shorter, which verifies the effectiveness of the algorithm. And through the matlab software for robotic arm simulation experiments, to verify the reliability of the time-optimized robotic arm trajectory.

关键词： robotic arm trajectory optimization time-optimal 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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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Liqin Liu 1 2 Xueliang Zhang 1 2 Liming Xie 1 Juan Du 2 1.College of Mechanical Electronic Engineering Lanzhou University of Technology 85 Langongping Lanzhou Gansu 730050 China 2.College of Mechanical Electronic Engineering Taiyuan University of Science and Technology 66 Waliu Road Taiyuan Shanxi 030024 China

In this article,a multi-objective particle swarm optimization algorithm based on dynamic crowding distance（DCD-MOPSO） was proposed,in which the definition of individual’s DCD was based on the degree of difference between the crowding distances on different *** proposed approach computed individual’s DCD dynamically during the process of population maintenance to ensure sufficient diversity amongst the solutions of the non-dominated *** 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 *** on well known and widely used test problems are performed,aiming at investigating the convergence and solution diversity of *** obtained results are compared with MOPSO and NSGA-Ⅱ,yielding the superiority of DCDMOPSO.

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

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Co-operative Vector-Evaluated particle swarm Optimization for Multi-objective Optimization

Co-operative Vector-Evaluated Particle Swarm Optimization fo...

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IEEE Symposium Series on Computational Intelligence

作者： Justin Maltese Beatrice M. Ombuki-Berman Andries. P. Engelbrecht Dept. of Comput. Sci. Brock Univ. St. Catharines ON Canada

ISBN: (纸本)9781479975617

Vector-evaluated particle swarm optimization is a particle swarm optimization variant which employs multiple swarms to solve multi-objective optimization problems. Recently, three variants of particle swarm optimization which utilize co-operative principles were shown to improve performance in single-objective environments. This work proposes co-operative vector-evaluated particle swarm optimization algorithms, which employ co-operative particle swarm optimization variants within vector-evaluated particle swarm optimization swarms. Performance of the proposed algorithms is compared with the standard vector-evaluated particle swarm optimization algorithm using various knowledge transfer strategies. A comparison of the best performing co-operative vector-evaluated particle swarm optimization variants is also made against well-known multi-objective PSO algorithms. Each co-operative vector-evaluated particle swarm optimization variant significantly outperforms standard vector-evaluated particle swarm optimization with respect to the hypervolume metric, with two of three variants also yielding improved solution distribution. The results indicate that co-operation is a powerful tool which enhances hypervolume and solution distribution of the original vector-evaluated particle swarm optimization algorithm, allowing co-operative vector-evaluated particle swarm optimization variants to successfully compete with top multi-objective PSO optimization algorithms.

关键词： particle swarm optimization particle swarm algorithm variants

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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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第十届电子商务、工程及科学领域的分布式计算和应用（DCABES）国际学术研讨会

作者： Gu Yuan-liang IoT Engineering Jiangnan University Wuxi,Jiangsu,China Xu Wen-bo IoT Engineering Jiangnan University Wuxi,Jiangsu,China

The development of digital integrated circuit has put forward urgent demands for test *** technology has become a bottleneck in the application of LSI/*** for sequential circuits,it is still a problem which is not resolved completely in *** 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 *** 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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