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5th International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET)

作者： AlKhafaji, Mohammed Ayad Uzun, Yunus Mazaya Univ Dept Elect Elect & Comp Engn Dhiqar Iraq Aksaray Univ Dept Elect Elect Engn Aksaray Turkey

ISBN: (纸本)9781728123011

This paper presents modeling and simulation for a pure electric vehicle with the propulsion chain. The PEV be composed of two electric motors to propulsion the vehicle rear wheels, voltage source inverter, a control system which controls both of motors speed and the electronic differential controller (EDC). Two synchronous reluctance motors represent the propulsion system to the EV. The Particle Swarm Optimization (pso) is used to specify the optimal parameters of the cascade controllers to control the electric motors. The specify driving route topology has designed and used to test vehicle validity under three different operation conditions. The vehicle system has simulated and tested in the Matlab/Simulink environment.

关键词： PEV EDC Driving route topology pso algorithm SVPWM Inverter

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Broadband MVDR Beamformer Applying pso 1

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1st International Conference on Swarm Intelligence

作者： Wang, Liang Song, Zhijie Ocean Univ China Dept Ocean Technol Qingdao 266100 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

In this paper, a broadband MVDR(minimum variance distortionless response) beamforming method based on time-domain (TMVDR) is presented. Using TMVDR beamformer, stable sample matrix estimation could be obtained in short time period. To obtain the stable optimum solution of TMVDR, a numerical searching method optimized by pso algorithm with constrain condition is introduced. Out-sea experiment show the performance of TMVDR beamformer applying pso algorithm proposed in this paper.

关键词： DOA estimation adaptive beamtorming pso algorithm

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A pso-Based Layout Method for GNSS Pseudolite System 2017

A PSO-Based Layout Method for GNSS Pseudolite System

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International Conference on Information Technology (ICIT)

作者： Wang, Jian Li, Hongxin Lu, Jinzhi Li, Kun Li, Huan Yang, Lian Li, Yubai Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China KTH Royal Inst & Technol S-10044 Stockholm Sweden

ISBN: (纸本)9781450363518

In order to improve the user positioning accuracy in GNSS (Global Navigation Satellite System) pseudolite system, we propose a pso (Particle Swarm Optimization)-based method to optimize the pseudolite layout in this paper. In detail, given the pseudolite layout information, we calculate the system GDOP (Geometric Dilution of Precision) and then minimize it by using a pso-based algorithm with N particles. Here the first particle indicates the classical layout under the given scenario and the other particles separately represent N-1 randomly generated layouts. In each iteration of our pso-based algorithm, these particles move to a direction to reduce the GDOP value. After several iterations, the GDOP value can be minimized and the optimal pseudolite layout is found out as well. To evaluate the merits of our method, we perform some experiments. The experimental results show that compared to the classical pseudolite layout, our method can reduce the GDOP by 13.4%. This, with no doubt, improves the user positioning accuracy. For example, when the pseudo-range error is about 1%, the user positioning accuracy in our optimized layout can be improved by 12.4% against the classical layout.

关键词： Pseudolite Layout Positioning GDOP pso algorithm

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Research and application of pso-based BP neural network in the project estimate of government investment

Research and application of PSO-based BP neural network in t...

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IITA International Conference on Control, Automation and Systems Engineering

作者： Liu, Wenhui Chi, Zhifeng Kashgar Teacher Coll Dept Informat & Technol Kashgar Peoples R China Xinyang Normal Univ Dept Urban & Environm Sci Xinyang Peoples R China

ISBN: (纸本)9780769537283

In order to increase the accuracy of the cost estimates in government investment, a method based on the pso trained neural network to estimate the cost is proposed. First the neural network model of a project cost estimate is created, and then pso is introduced to optimize the weight and threshold of the neural network, at last the neural network trained is used to estimate cost of the project. The results show that this method not only overcome the defects of traditional neural network such as learning for a long time and shock, but also increase the accuracy of the cost of the project to estimate.

关键词： pso algorithm BP neural network Optimization Cost estimates

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Hybrid Energy Storage Strategy Based on MPC and pso-VMD to Stabilize Wind Power Fluctuation 7

Hybrid Energy Storage Strategy Based on MPC and PSO-VMD to S...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Huo, Junda Wang, Yi Meng, Jianhui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding Peoples R China

ISBN: (纸本)9798350375794;9798350375800

In order to stabilize the original power fluctuation of wind power and avoid threatening the stable operation of power grid, a strategy of applying hybrid energy storage to stabilize wind power fluctuation is proposed. Firstly, the model predictive control is used to stabilize the wind power fluctuation in real time, and the wind power grid-connected power and the total output of the hybrid energy storage system that meet the requirements are obtained. Then, pso algorithm is used to optimize the parameters of VMD algorithm, and VMD algorithm is used to realize the initial allocation of HESS power. Finally, aiming at the problem of SOC over-limit of energy storage, the initial distribution instruction is optimized by fuzzy control method. The results of the example analysis show that the strategy proposed in this paper can effectively solve the fluctuation problem of the original power of wind power and protect the energy storage.

关键词： model predictive control pso algorithm variational mode decomposition fuzzy control

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pso-BP Neural Network Based Prediction Study for Nonlinear Problems

PSO-BP Neural Network Based Prediction Study for Nonlinear P...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Nan, Jiedong Medical College of Tianjin University Tianjin China

ISBN: (纸本)9798350309614

The Back Propagation (BP) neural network is commonly used for predicting nonlinear problems. However, this model is known to converge slowly and is prone to local optimisations. To address this issue, using a population intelligence optimisation algorithm to optimise the structure of the BP neural network is highly significant. This paper aims to explore the prediction of nonlinear problems by establishing the prediction model according to the three-layer architecture mode of BP neural network and using the characteristics of global optimisation of the particle swarm algorithm (particle swarm optimization, pso) to optimise the initial weights and thresholds of BP neural network, so as to propose a kind of prediction model of nonlinear problems based on pso-BP neural network. Based on the experimental results, the coefficient of determination R2 of the pso-BP neural network is 29.41% higher than that of the single BP neural network. Moreover, the RMSE is reduced by 64.08%. These findings demonstrate that the pso algorithm-optimised BP neural network has superior prediction accuracy and stability for nonlinear problems than that of the single BP neural network. This provides constructive optimisation of the structure of the BP neural network. © 2023 IEEE.

关键词： BP neural network nonlinear prediction problem particle swarm prediction model pso algorithm

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Embedded feature fusion for multi-label criteria selection via local search strategy and particle swarm optimization

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Scientific reports 2025年第1期15卷 16508页

作者： Suhua Chen Xu Fang Feng Zhai Li Wang Lin Lv School of Electrical Engineering Xuchang University Xuchang 461000 Henan China. heroncsh216@***. Henan Xj Metering Co. Ltd. Xuchang 461000 Henan China. China Electric Power Research Institute Beijing 300072 China. State Grid Shandong Electric Power Company Gaomi Power Supply Company Weifang 261505 Shandong China.

Multi-label classification is a significant challenge in machine learning, especially as the dimensionality of the problem increases. As the number of dimensions grows, the performance of traditional classification algorithms often degrades substantially. Feature selection is a key technique for reducing dimensionality in multi-label scenarios, operating as a non-parametric process. Despite its importance, feature selection remains a complex issue without straightforward solutions, and various approaches using AI and evolutionary algorithms have been proposed to tackle it. However, these methods typically suffer from reduced efficiency and slower convergence as the dimensionality increases, due to the expanding search space. To address this issue and enhance convergence speed, this article introduces a hybrid AI solution that combines a binary particle swarm optimization algorithm with a local search strategy specifically designed for multi-label feature selection. Within this local search strategy, feature fusion plays a crucial role, where features are merged based on their relevance and correlation with the problem's output. These fused features are divided into two categories: those directly associated with the problem class and those that are similar to the problem class but distinct from other feature fusions. By leveraging this categorization, the particle swarm optimization technique is augmented with a local operator that removes redundant feature fusions and refines each solution. By incorporating this operator, the proposed method achieves superior convergence speed compared to previous algorithms in the field. The performance of the proposed method was evaluated across several datasets against some of the most widely used feature fusion selection algorithms. The experimental results demonstrated the proposed method's accuracy and efficiency, validating its effectiveness in multi-label feature selection.

关键词： Collective intelligence Feature fusion Filter type Local search strategy Multi-label classification pso algorithm

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Stacking sequence optimization of composite plates for maximum fundamental frequency using particle swarm optimization algorithm

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MECCANICA 2012年第3期47卷 719-730页

作者： Bargh, H. Ghashochi Sadr, M. H. Amirkabir Univ Technol Ctr Excellence Computat Aerosp Engn Dept Aerosp Engn Tehran Iran

The paper illustrates the application of the particle swarm optimization (pso) algorithm to the lay-up design of symmetrically laminated composite plates for maximization of fundamental frequency. The design variables are the fiber orientation angles, edge conditions and plate length/width ratios. The formulation is based on the classical laminated plate theory (CLPT), and the method of analysis is the semi-analytical finite strip approach which has been developed on the basis of full energy methods. The performance of the pso is also compared with the simple genetic algorithm and shows the good efficiency of the pso algorithm. To check the validity, the obtained results are compared with those available in the literature and some other stacking sequences, wherever possible.

关键词： Composite plates pso algorithm Finite strip Optimization

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New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search algorithm

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WATER RESOURCES MANAGEMENT 2019年第7期33卷 2335-2356页

作者： Meshram, Sarita Gajbhiye Ghorbani, M. A. Deo, Ravinesh C. Kashani, Mahsa Hasanpour Meshram, Chandrashekhar Karimi, Vahid Rani Durgavati Univ Dept Math & Comp Sci Jabalpur India Univ Tabriz Dept Water Engn Tabriz Iran Near East Univ Fac Engn 10 Mersin TR-99138 Nicosia North Cyprus Turkey Univ Southern Queensland Inst Agr & Environm Sch Agr Computat & Environm Sci Springfield Qld 4300 Australia Univ Mohaghegh Ardabili Dept Water Engn Ardebil Iran

Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear, artificial intelligence (AI) models for optimal prediction can be far reaching in real-life decision support systems. AI-based models are considered to be favorable predictive tools since the nonlinear nature of suspended sediment data series warrants the utilization of nonlinear predictive methods for feature extraction, and for accurate simulation of suspended sediment load. In this study, Artificial Neural Network (ANN) approaches are employed to estimate the monthly sediment load where the two-phase Feed-forward Neuron Network Particle Swarm Optimization Gravitational Search algorithm (FNN-psoGSA) is developed, and then evaluated in respect to 3 distinct algorithms: the Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed-forward Neuron Network (FNN) and the single-phase Feed-forward Neuron Network Particle Swarm Optimization (FNN-pso). The study is carried out using the monthly rainfall, runoff and sediment data spanning a 10year period (2000-2009) where about 75% of data are used in model training phase, 25% in testing phase. Three statistical performance criteria namely: the mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE) and the Willmott's Index (WI) and diagnostic plots visualizing the tested results are used to evaluate the performance of four AI-based models. The results reveal that the objective model (the two-phase FNN-psoGSA model) and the single-phase FNN-pso model yielded more precise results compared to the other forecast models. This result accorded to an NSE value of 0.612 (for the FNN-psoGSA model) vs. an NS value of 0.500, 0.331 and 0.244 for the FNN-pso, FNN and ANFIS models, and WI=0.832 vs. 0.771, 0.692 and 0.726, respectively The study also demonstrated that the FNN model generated slightly better results than the ANFIS model for the estimation of sediment load data but over

关键词： Neural networks pso algorithm GSA algorithm Sediment load Modelling

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A new approach to particle swarm optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 844-854页

作者： Gosciniak, Ireneusz Univ Silesia Inst Comp Sci PL-41200 Sosnowiec Poland

Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution. (C) 2014 Published by Elsevier Ltd.

关键词： Co-evolutionary systems pso algorithm Predator-prey algorithm Immune algorithm Optimization method Games Artificial intelligence Entropy Multifractal analysis

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