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Optimization and experimental analysis of sweet potato ship-shaped transplanting trajectory using particle swarm algorithm

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 100-107页

作者： Zhengduo Liu Lin Hua Zhaoqin Lyu Jiaji Hu Wenxiu Zheng College of Mechanical and Vehicle Engineering West Anhui UniversityLu’an 237000AnhuiChina Traditional Chinese Medicine Institute of Anhui Dabie Mountain West Anhui UniversityLu’an 237012AnhuiChina College of Mechanical and Electronic Engineering Shandong Agricultural UniversityTai’an 271018ShandongChina Anhui Zhengyang Machinery Technology Co. Ltd.Lu’an 237012AnhuiChina

The mechanization of ship-shaped transplanting is currently an urgent problem that should be *** movement trajectory of the transplanting mechanism is the key technology to perform ship-shaped *** this study,a ship-shaped transplanting trajectory was built and a mathematical model of a four-link transplanting mechanism was developed based on the requirements of the sweet potato transplanting agronomic *** particle swarm optimization algorithm was used,with the length of the four-bar mechanism and the installation angle of the fixed bar serving as the variables to *** objective was to minimize the deviation of the ship-shaped transplanting trajectory,yielding an iterative optimization *** MATLAB simulation results showed that the penalty factors of different proportions in the adaptive particle swarm optimization algorithm affected the transplanting *** optimal penalty factor parameters areα=0.6,β=*** ensure that the transplanting trajectory fulfills the agronomic requirements,and limit the deviation in the return trajectory of the *** sizes of the optimized four-bar mechanism were 110,312,245,160,360,and,160 *** determined installation angle was 100°.The results of the field experiments demonstrated that the optimized four-bar transplanting mechanism can better fulfill the agronomic technical requirements of sweet potato ship-shaped *** a transplanting speed of 0.2 m/s,the average qualified rates of insertion depth,insertion length,and tail height were equal to 94.00%,93.83%,and 91.67%.The results obtained in this study provide a theoretical basis and technical support for studying and developing sweet potato ship-shaped transplanting machinery.

关键词： sweet potato oblique insertion method mechanical arm particle swarm

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Autotuning algorithm of particle swarm PID parameter based on D-Tent chaotic model

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Journal of Systems Engineering and Electronics 2013年第5期24卷 828-837页

作者： Min Zhu Chunling Yang Weiliang Li School of Electrical Engineering and Automation Harbin Institute of Technology China FAW Group Corporation R&D Center

An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.

关键词： D-Tent particle swarm proportional-integral- derivative (PID) parameter optimization.

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particle swarm Optimization with Adaptive Mutation

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Frontiers of Electrical and Electronic Engineering in China 2006年第1期1卷 99-104页

作者： LU Zhen-su HOU Zhi-rong DU Juan School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China

A new adaptive mutation particle swarm optimizer,which is based on the variance of the population's fitness,is presented in this *** the running time,the mutation probability for the current best particle is determined by two factors:the variance of the population's fitness and the current optimal *** ability of particle swarm optimization(PSO)algorithm to break away from the local optimum is greatly improved by the *** experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO,but can also avoid the premature convergence problem effectively.

关键词： particle swarm adaptive mutation optimization premature convergence

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Sliding-Mode PID Control of UAV Based on particle swarm Parameter Tuning

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Computers, Materials & Continua 2020年第4期63卷 469-487页

作者： Yunping Liu Xingxing Yan Fei Yan Ze Xu Weiyan Shang School of Automation Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Atmospheric Environment and EquipmentNanjing210044China Department of Mechanical and Industrial Engineering Ryerson UniversityTorontoM5B 2K3Canada Department of Mechanical and Manufacturing Engineering University of ManitobaWinnipegR3T 5V6Canada

Due to the coupled motion between the rotor unmanned aerial vehicle(UAV)and the manipulator,the underactuation characteristics of the system itself,and the influence of external uncertainties,the stability of the rotor UAV’s manipulator control system is difficult to *** on the dynamic model of the rotor UAV,the stability of the whole UAV manipulator control system is improved by using the piecewise cost function,the compression factor particle swarm optimization(PSO)algorithm and the sliding mode PID to establish the sliding mode PID control stability method based on the *** with the sliding mode PID control method,this method solves the serious buffeting problem in the sliding mode control,reduces the influence of the external disturbance and realizes the attitude stabilization control of the UAV manipulator quickly and accurately,thus shortens the system adjustment time and improves the anti-interference ability.

关键词： Manipulator dynamic model compression factor particle swarm sliding mode PID UAV

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Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

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Journal of Integrative Medicine 2021年第5期19卷 395-407页

作者： Liang Ding Xin-you Zhang Di-yao Wu Meng-ling Liua School of Computer Jiangxi University of Traditional Chinese MedicineNanchang 330004Jiangxi ProvinceChina School of Pharmacy Jiangxi University of Traditional Chinese MedicineNanchang 330004Jiangxi ProvinceChina

Objective: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer(PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine(TCM) ***: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining10,060 electronic medical records, which were randomly divided into a training set and a test *** on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification ***: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%,respectively. The classification accuracy rates of the models for all syndromes in this paper were between82.15% and 93.82%.C

关键词： Primary liver cancer Syndrome type particle swarm Extreme learning machine Fuzzy mathematics

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Research on Robot Tracking of Books Returning to Bookshelf Based on particle swarm Optimization Fuzzy PID Control

Research on Robot Tracking of Books Returning to Bookshelf B...

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第32届中国控制与决策会议

作者： Ren Xiaolin Yang Yi Gao Long Chen Jianbo Mei Tianxiang Yu Jingya Han Qinqin College of Electrical Engineering Nantong University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to avoid library intelligent returning books to bookshelf robot during the process of searching for books and bookshelves,the tracking time is long and the path deviation is *** system uses a navigation system based on a fuzzy PID controller based on particle swarm optimization to avoid problems such as empirical parameter selection and parameter invariance in fuzzy PID *** particle swarm optimization algorithm is used to optimize the controller parameters,enhance the controller’s adaptive ability,and improve its control *** show that the system improves the accuracy of the path,and has a faster response speed and higher tracking accuracy when moving.

关键词： Track particle swarm Fuzzy PID control Adaptive

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Optimization of Roll Thermal Filed Model Based on particle swarm Algorithm

Optimization of Roll Thermal Filed Model Based on Particle S...

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第十届国际轧钢大会

作者： Liu Jian Wang Dan He Wei Ya Nan (MMC Capital Engineering & Research Incorporation Limited Beijing 100176 China)

＜正＞According to working condition of roll in the phases of rolling,water cooling,air cooling, the key heat exchanging factors of roll boundary conditions,including the contacting heat conduction between roll and plates,plastic deformation heat of metals,friction heat between roll and plates, convecting heat between roll and water,convecting heat between roll and air,is considered *** that basis,thermal field model of roll is established by 2 - D finite explicit difference *** improving the accuracy of roll thermal field model,particle swarm algorithm is used to optimize the undefined key factors affecting the boundary conditions,and gets accurate thermal crown prediction of roll for plate shape control.

关键词： thermal field particle swarm thermal crown

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X-ray Coal Purity Examination Base on particle swarm Optimization Clustering

X-ray Coal Purity Examination Base on Particle Swarm Optimiz...

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2013教育技术与信息系统国际会议

作者： Long Hao Huo Na Yang Yong Yu Bencheng Xuzhou College of Industrial Technology

the paper mainly researched on the usage of particle swarm in image clustering. A fitness function is designed based on the principle of minimum in cluster distance and maximum between cluster distance. And particle swarm optimization, which is combined with fuzzy clustering, is used in image clustering. A system of X-ray coal image purity detection is developed and tested. According to the intensity distribution of X-ray images, an area of interest is selected based on Gaussian distribution property which greatly cut down the data to be processed. On the basis of that, particle swarm clustering is applied, and impurities are selected based on the fact that impurities are always tiny intensity elements. Experiments show that the approach is effective.

关键词： coal purity particle swarm clustering analysis X-ray fitness function

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A Novel particle swarm Optimization Algorithm with Self-adaptive Inertia Weight

A Novel Particle Swarm Optimization Algorithm with Self-adap...

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第二十四届中国控制会议

作者： Zhang Xueliang, Wen Shuhua, Li Hainan, Liu Shuyang, Wu Meixian, Wang Jiaying (Taiyuan University of Science & Technology, Taiyuan, 030024 )

＜正＞ A novel particle swarm optimization algorithm with self-adapting inertia weight （SAIWPSO）, which is based on the variance of the population’s fitness, is presented. During computing, the inertia weight is determined adaptively and randomly according to the variance of the population’s fitness. The ability of particle swarm optimization algorithm（PSO） to break away from the local optimum is greatly improved by the self-adapting inertia weight. The experimental results show that this algorithm not only has great advantage of convergence properly over PSO, but also can avoid the premature convergence problem effectively.

关键词： particle swarm self-adapting inertia weight optimization premature convergence

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Clustering Algorithm for Mixed Attributes Data Based on Restricted particle swarm Optimization

Clustering Algorithm for Mixed Attributes Data Based on Rest...

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2011 International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Zhang Tiejun Yang Jing Zhang Jianpei Department of Computer Science Harbin Engineering University Department of Computer Science Northeast Agricultural University

ISBN: (纸本)9781612848334

When the data mixed with numerical and categorical values is processed at present, it is very common to convert the categorical values into numerical ones and then cluster them according to a certain weight. Obviously such clustering results rely heavily on the weight given by experts. Hence in this paper a categorical attribute is proposed as a potential field restriction to limit the searching directions of particle swarm, which consequently improves the speed and effectiveness of the clustering algorithms.

关键词： numerical and categorical values potential field restriction particle swarm

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