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6th World Congress on Intelligent Control and Automation

作者： Cao, Chunhong Zhang, Bin Li, Xiaolin Wang, Limin Li, Wenhui Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)1424403316

When transferring the geometric constraint equation group into the optimization model, we need a method to jump out of the local beat solution so that we can find a global best solution. Considering the speed and global capability, we adopt compound particle group optimization algorithm. particle swarm optimization algorithm is a kind of evolution computation technology based on group intelligence. In all the evolution computations heuristic function should be included to control its one's own characteristic. These parameters are usually correlated with the specific problem and are defined by the users. Suitable parameter choice needs user abundant experience and correct judgment on the information offered by the problem. More important thing is that these heuristic parameters will influence the convergence characteristic of the algorithm. Because of this even experienced users may choose the not appropriate parameter and then make the problem unable to get effective solution. It needs to carry on some research on these parameters more and more. Here we choose the control parameters as an optimization question in the particle swarm algorithm. Thus heuristic function in the PSO can be controlled by the ordinal genetic algorithm and we form the composite particle swarm optimization algorithm. And we use this algorithm into the geometric constraint solving successfully.

关键词： geometric constraint solving group intelligent algorithm particle swarm optimization algorithm composite particle swarm optimization algorithm

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Pass Design and optimization Based on Finite Element Modeling

Pass Design and Optimization Based on Finite Element Modelin...

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32nd Chinese Control Conference (CCC)

作者： Xiao Cheng Wu Min Chen Xin Jin Feng Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Engn Lab Adv Control & Intelligent Automat Changsha 410083 Hunan Peoples R China Northeast Power Co Ltd Shenyang 110181 Peoples R China

ISBN: (纸本)9781479900305

The deformation of seamless steel pipe rolling process is complex due to material and geometrical nonlinearity, so it's hard to design pass and optimize rolling schedule. With the analysis of rolling for PQF, a method of pass design and optimization method based on the finite element modeling is presented. On the basis of pass design, the finite element simulation model is established. Considering the checking condition of pass design and rack load balanced allocation, the paper establishes an appraisal model based on the finite element modeling to evaluate pass design. Finally, as the evaluation function serves as the objective function, the paper uses particle swarm optimization algorithm to optimize specific elongation. These not only develop new products but also enhance the state rolling process characteristics (dimensional accuracy and internal and external surface quality).

关键词： Pass design Finite element modeling Appraisal model particle swarm optimization algorithm Elongation

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SCP model based on the new energy vehicle collaborative detection configuration method research

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhou, Mingyang Qiu, Chengcheng Chongqing Jiaotong Univ Coll Aviat Chongqing 400000 Peoples R China Nanjing Audit Univ Sch Accounting Nanjing 210000 Jiangsu Peoples R China

To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2 similar to 5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km similar to 3km. 5 similar to 10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

关键词： SCP model particle swarm optimization algorithm Radar and infrared sensor Cooperative vehicle detection configuration Two-layer cooperative guidance

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AN IMPROVED NEURAL NETWORK ADAPTIVE SLIDING MODE CONTROL USED IN ROBOT TRAJECTORY TRACKING CONTROL

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第5期11卷 1655-1666页

作者： Liu, Tianhua Yin, Shoulin Shenyang Normal Univ Software Coll 253 North Huanghe St Shenyang 110034 Liaoning Peoples R China

This paper proposes an improved neural network adaptive sliding mode control method based on the neural network sliding mode control to improve the performance of the robot trajectory tracking control. This new scheme regards neural network as a controller and adopts robust control law to eliminate the approximation error which uses its nonlinear mapping ability to approximate various unknown nonlinear systems. Hidden layer units and network structure parameter have an effect on neural network mapping. So we will decrease chattering. We conduct experiments with three joints robot and give an example to verify this paper's scheme under the MATLAB platform. The results of experiments show that the new neural network adaptive sliding mode control has a good control accuracy and robustness than other control methods. The new scheme reduces the chattering effectively and also decreases the effect of hidden layer units and network structure parameter on neural network mapping. It will be a good choice for the robot trajectory tracking control.

关键词： Robot tracking Neural network Neural network adaptive sliding mode control particle swarm optimization algorithm Chattering

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Quantitative determination of TVB-N content for different types of refrigerated grass carp fillets using near-infrared spectroscopy combined with machine learning

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 126卷

作者： Zhang, Qinyu Hu, Zhigang Xu, Zijian Zhang, Peilin Jiang, Yajun Fu, Dandan Chen, Yan Wuhan Polytech Univ Sch Mech Engn Wuhan 430023 Hubei Peoples R China Hubei Prov Cereal & Oil Machinery Engn Technol Res Wuhan 430023 Hubei Peoples R China Hubei Prov Aquat Prod Proc Equipment Engn Technol Wuhan 430023 Hubei Peoples R China

This research aims to accurately forecast the freshness indicators (TVB-N) of skinned and skinless grass carp fillets by integrating near-infrared spectroscopy (NIR) with machine learning algorithms. By comparing the predictive accuracy of machine learning models for the two types of grass carp fillets, the most effective modeling method is identified. Methodologically, the study first applies orthogonal signal correction (OSC) and the first derivative among other algorithms for spectral pre-processing. Subsequently, competitive adaptive reweighted sampling (CARS), moving window partial least squares (MWPLS), and random frog (RF) are used for the selection of variables. Lastly, partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural networks (BPNN), and particle swarm optimization-enhanced BP neural networks (PSO-BP) are employed to quantitatively analyze the NIR data. The most relevant results reveal that the (OSC+D1)-CARSPSO-BP model exhibits superior predictive capabilities. Especially when applied to skin-on fish fillets (R2P =0.988, RMSEP=0.092), this model surpasses that for skinless fish fillet data (R2P =0.987, RMSEP=0.096). Therefore, combining near-infrared with machine learning to predict the freshness (TVB-N) of grass carp fillets based on skin-on samples is a more effective non-destructive testing method.

关键词： Near -Infrared spectroscopy Rapid non-destructive detection Grass carp fillets Total volatile basic nitrogen Machine learning Orthogonal signal correction (OSC) particle swarm optimization algorithm optimized BP neural network (PSO-BP)

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Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Li, Chengjie Wang, Yanglin Meng, Linghui Zhong, Wen Zhang, Chengfang Liu, Tao Southwest Minzu Univ Sch Comp & Artificial Intelligence Key Lab Comp Syst State Ethn Affairs Commiss Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Chengdu Third Peoples Hosp Dept Gen Med Chengdu 610014 Peoples R China Sichuan Police Coll Intelligent Policing & Natl Secur Risk Management Intelligent Policing Key Lab Sichuan Prov Luzhou Peoples R China

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring.

关键词： particle swarm optimization algorithm Simulated annealing algorithm Principal component analysis Elimination strategy Feature engineering Cardiovascular disease (CVD)

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Soft Sensor Modeling of Melt-Index of High Pressure Low-Density Polyethylene

Soft Sensor Modeling of Melt-Index of High Pressure Low-Dens...

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20th Chinese Control and Decision Conference

作者： Bu Yan-ping Yu Jin-shou E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)9781424417339

On the basis of analyzing the particle swarm optimization (PSO) algorithm and support vector machine (SVM), the PSO algorithm with chaos searching is applied to optimize the parameters of SVM, then the PSO-SVM model about a practical soft-sensor of melt-index of High Pressure Low-Density Polyethylene is constructed. The method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO for soft sensor modeling. The simulation results demonstrate that the model has effective generalization performance, higher precision and engineering practicability.

关键词： particle swarm optimization algorithm chaos support vector machine soft-sensor melt-index

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Multi-criteria optimal sizing and analysis of PV/wind/fuel cell/battery/diesel generator for rural electrification: A case study in Chad

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2024年第3期13卷 491-507页

作者： Abdoulaye, Mahamat Adoum Waita, Sebastian Wekesa, Cyrus Wabuge Mwabora, Julius Mwakondo Univ Nairobi Dept Phys POB 30197-00100 Nairobi Kenya Korea Inst Energy Res Jeju Global Res Ctr Distributed Energy Team 200 Haemajihaean Ro Jeju 63357 South Korea Univ Eldoret Sch Engn POB 1125-30100 Eldoret Kenya

. Access to sustainable, clean, affordable, and reliable electricity is crucial for social and economic development, yet Sub-Saharan Africa (SSA) struggles significantly in this context. In CHAD, only 11.3% of the population is able to access electricity, making it one of the least electrified countries in SSA with the lowest clean energy access. In rural areas, electricity access falls to just 1.3%. This research applies and executes a MultiObjective particle swarm optimization (MOPSO) algorithm using MATLAB R2023b to assess the techno-economic, environmental, and social impacts of a hybrid system based on optimal PV/Wind/Battery/Fuel Cell (FC)/Diesel generator (DG) sizing for rural electrification in CHAD. The proposed system's self-sufficiency index (SSSI) and the Annualized System Cost (ASC) were chosen as objective functions to guarantee the economic feasibility of the system, higher self-sufficiency, and lower dependence on external energy sources (DG). The simulation results show that the optimal size of the proposed system supplies the load demand by 100% of the renewable energy sources (RES) fraction, and the optimal capacities of the main components to supply the load demand are: Solar Power (493 KW), Wind Turbine (166 KW), Battery Energy Charge/Discharge (229180 kWh /221300 kWh), Hydrogen tank storage energy (83 874 kWh), Electrolyzer size (202 KW), Fuel cell size (144 KW). The evelized cost of electricity (LCOE) of 0.2982 $/kWh, which is 51.12% lower than the national unit production costs of electricity in rural areas of CHAD (0.61 $/kWh). This LCOE is also the lowest compared to previous works done using HOMER Pro for the country of CHAD. The results also give a levelized cost of hydrogen (LCOH) of 3.8563 US $/kg, lower than for all studies found in the literature for the country of Chad. The proposed system's yearly avoided greenhouse gas (GHG) emission is 374 640 kg. The proposed system will create five (5) new jobs (JCO) and improve the Human Develo

关键词： system particular Avoided greenhouse gas (GHG) social assessment Hybrid energy system Optimal sizing rural electrification particle swarm optimization algorithm zero-carbon electricity

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Study on Dynamic Attribute Reduction Based on Improved PSO algorithm

Study on Dynamic Attribute Reduction Based on Improved PSO A...

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7th World Congress on Intelligent Control and Automation

作者： Xia, Kewen Zhang, Ling Wu, Pinghui Zhang, Xinying Hebei Univ Technol Sch Informat Engn Tianjin 300401 Peoples R China

ISBN: (纸本)9781424421138

Attribute reduction is one of the important topics in the research on rough set theory. In confrontation with dynamic data, common methods of attributes reduction have such disadvantages as unstable reduction results, intensive computation and the difficulty in meeting the need of real-time processing. To solve these problems, a method of dynamic attributes reduction with improved PSO algorithm is proposed based on the research of particle swarm optimization. The concrete work is the following: firstly, the traditional PSO algorithm is improved to enhance the global search ability, which increases the diversity of particle population distribution. Secondly, the information decision system data extraction is divided into some subdecision tables, which are reducted by used of improved PSO algorithm. Finally, each reduction results are intersected and get the most minimal reduction. Simulation and experimental results show the dynamic reduction algorithm can overcome the shortcomings of common attribute reduction which possesses the significant effect and rapid computation.

关键词： Rough Set Dynamic Attributes Reduction particle swarm optimization algorithm

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Optimum Design of PI Controller for Brushless DC Motor Based on PSO-CS algorithm 22

Optimum Design of PI Controller for Brushless DC Motor Based...

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Proceedings of the 2022 International Conference on Big Data, IoT, and Cloud Computing

作者： Silin Cui Xing Liu Shoushan Jiang Wei Wu School of Mechanical Engineering Northwestern Polytechnical University China School of Electronic Information Engineering Xi'an Technological University China

ISBN: (纸本)9781450399548

Aiming at the problems of slow response and low accuracy of traditional brushless DC motor (BLDCM) speed control system, the control strategy for the BLDCM based on PSO-CS fusion optimization algorithm to optimize the parameters of motor PI controller is proposed. Firstly, the BLDCM's double closed-loop control system mathematical model is established. Secondly, based on PSO-CS fusion optimization algorithm, a PI controller parameter optimization method is designed to determine the optimal parameters for motor speed control. Finally, the BLDCM control system model is built in MATLAB / Simulink. The current loop adopts traditional PI control, and the speed loop is controlled by traditional PI, PSO PI and PSO-CS PI respectively. The operation of the motor under different working conditions is simulated. The research shows that compared with the basic PSO algorithm, the PSO-CS fusion optimization algorithm has higher computational accuracy, and the parameter values obtained by using PSO-CS algorithm to optimize PI controller are better. At the same time, the PSO-CS PI controller has a better effect on BLDCM speed control. Also, it has low overshoot and short adjusting time. It shows that the proposed control strategy can make the BLDCM system has good robustness and stability.

关键词： BLDCM Cuckoo search algorithm PI control particle swarm optimization algorithm

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