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Nonlinear System Identification Using Adaptive Volterra Model Optimized with Sine Cosine algorithm

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14411-14422页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttarakhand India

This paper presents the handling of nonlinear system identification problem based on Volterra model. Gradient-based algorithms are generally applied to solve system identification problems. However, these algorithms have the limitation of getting trapped in the local minimum. In the presented work, a novel population-based optimization algorithm popularly known as sine cosine algorithm (SCA) is being utilized for the identification of nonlinear discrete-time system. The SCA uses mathematical sine and cosine functions for the purpose of optimization. SCA is responsible for the creation of multiple random solutions and moving them towards best solution while maintaining proper balance between the exploitation and exploration phases of optimization. The performance evaluation of the applied SCA is carried out in terms of coefficient evaluation, mean square error and convergence profile. Two different examples for nonlinear system are presented in this work so as to demonstrate the validity of the employed algorithm. Performance analysis of the proposed approach with the existing state-of-the-art algorithms proves that the SCA outperforms the other algorithms.

关键词： Nonlinear system Volterra model Sine cosine algorithm Gravitational search algorithm particle swarm optimization algorithm Cuckoo search algorithm

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Application of Support Vector Machine Based on particle swarm optimization in Short-Term Load Forecasting of Honghe Power Network

Application of Support Vector Machine Based on Particle Swar...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Hua, Jing Xiong, Wei Niu, Lin Cao, Linlin Honghe Univ Engn Coll Mengzi 610000 Yunnan Peoples R China

ISBN: (纸本)9783030152352;9783030152345

In order to predict short-term load accurately and effectively, a short-term load forecasting model (PSO-SVM) based on particle swarm optimization (PSO) and support vector machine (SVM) is proposed. The parameters of the support vector machine are regarded as the velocity and position of a particle, and the optimal support vector machine parameters are found through continuous updating of the speed and position of the example. It can overcome support vector machine algorithm's shortcoming. The model of short-term load forecasting of the Red River power grid is established according to the optimal parameters, and the model performance is simulated. Try. The simulation results show that, compared with the SVM prediction model, PSO-SVM not only speeds up the optimization speed of SVM, but also improves the precision of load forecasting, and is more suitable for the need of short-term load forecasting in regional power grid.

关键词： Short term load forecasting Support vector machine particle swarm optimization algorithm

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Digital Twin System for Home Service Robot Based on Motion Simulation

Digital Twin System for Home Service Robot Based on Motion S...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Jiang, Zhengsong Tian, Guohui Cui, Yongcheng Liu, Tiantian Gu, Yu Wang, Yifei School of Control Science and Engineering Shandong University Jinan China School of Information University of California Berkeley United States

ISBN: (纸本)9781665430654

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical. © 2023 IEEE.

关键词： Digital Twin Motion Simulation particle swarm optimization algorithm Service Robot

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UAV Localization Model Based on Multi-Dimensional Eigenvalue-Kalman Filter 6

UAV Localization Model Based on Multi-Dimensional Eigenvalue...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Tian, Mengxian Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350305623

With the rapid development of science and technology, drone technology is gradually becoming an indispensable part of human life. With the increasing demand for complex tasks of UAVs from all walks of life, a single UAVs will not be able to effectively complete the task, and UAVs cluster gradually comes into people's perspective, and the resulting positioning of UAVs cluster has become the focus of current research. Azimuth-only passive positioning is a method that can adjust the position of UAVs and maintain formation. Several drones in the team send signals, and the rest of the drones passively receive signals to obtain direction information for positioning, and then adjust the position of the drones. This paper establishes Kalman filter algorithm model based on multidimensional eigenvalues, path planning model based on Bayesian estimation, and uses triangulation positioning method, particle swarm optimization algorithm and other methods to optimize azimuthing-only passive positioning of UAVs in formation flight. This method effectively improves the accuracy of UAV positioning and plays an important role in solving practical problems. © 2023 IEEE.

关键词： Bayesian estimation path planning model Kalman filter algorithm particle swarm optimization algorithm triangulation positioning method

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Response-Compensated DMPC-Based Frequency Control Strategy for AGC with Large-Scale Wind Power

Response-Compensated DMPC-Based Frequency Control Strategy f...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Zhang, Peng Mo, Zhonghua Mastoi, Muhammad Shahid Yang, Yinghai Cai, Shaorong Hassan, Mannan Wang, Delin The Southwest Branch of State Grid Corporation Sichuan Chengdu610102 China School of Electrical Engineering Southwest Jiaotong University Sichuan Chengdu611756 China

ISBN: (纸本)9798350322699

This paper proposes an AGC frequency control strategy using response-compensated distributed model predictive control (DMPC) when large-scale wind power is incorporated into the automatic generation control (AGC) of power systems to optimize the response speed of frequency modulation control systems based on model predictive control. It examines the influence of AGC system parameters on wind power penetration rate based on frequency control models for conventional units and doubly-fed wind turbines. A response compensation link is added to DMPC, and the particle swarm optimization algorithm is used to set the control parameters of the optimization link and analyze the stability of the system after the link is added. A frequency control strategy based on response compensation DMPC is then proposed based on system frequency deviation. Simulations are conducted to verify the AGC model of a regional interconnected power system with large-scale wind power. Results indicate that the proposed control strategy can improve system frequency stability and speed up frequency control response times. © 2023 IEEE.

关键词： automatic generation control frequency stability large-scale wind power model predictive control particle swarm optimization algorithm

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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

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ATMOSPHERE 2024年第3期15卷 351页

作者： Song, Fucheng Shi, Shuangshuang Linyi Univ Coll Resources & Environm Shandong Prov Key Lab Water & Soil Conservat & Env Linyi 276000 Peoples R China China Univ Min & Technol Jiangsu Key Lab Resources & Environm Informat Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008-2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4-22.1%, 0.1-12.8%, and 0.2-26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5-13.6%, 0-10.1%, and 0-16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model.

关键词： China ionosphere maps total electron content spatial gridding approach particle swarm optimization algorithm artificial neural network

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A New particle swarm optimization algorithm to Hierarchy Multi-objective optimization Problems and Its Application in Optimal Operation of Hydropower Stations

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JOURNAL OF COMPUTERS 2012年第8期7卷 2039-2046页

作者： Yang, Junjie Zhanjiang Normal Univ Sch Informat & Technol Zhanjiang Peoples R China

Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.

关键词： multi-objective optimization particle swarm optimization algorithm daily generation scheduling

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Principle and Application Research of particle swarm optimization 5

Principle and Application Research of Particle Swarm Optimiz...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhang, Zhiqiang Wang, Le Hu, Jiongsong Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Univ South China Sch Comp Sci Hengyang Peoples R China

ISBN: (纸本)9781665423144

With the increasing size and complexity of modern industrial production, the requirements for control systems are becoming higher and higher. The particle swarm optimization algorithm is a population-based stochastic optimization method. This paper first introduces the origin, principle and implementation steps of PSO algorithm, and then introduces the application of particle swarm algorithm in control system. In this paper, the fuzzy control strategy based on particle swarm optimization is proposed to improve the performance of fuzzy control. The design idea and implementation of the control strategy are described in detail. Finally, the system simulation is carried out with Matlab, and the simulation results are compared and analyzed. The results show that the control strategy can effectively improve the dynamic quality and steady-state accuracy of the control system, and has a good practical application prospect.

关键词： particle swarm optimization algorithm principle chaotic control system system simulation

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Application of particle swarm optimization in parameter identification of power electronic circuits 5

Application of particle swarm optimization in parameter iden...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Lai, Guosheng Jiangxi Univ Engn Xinyu 338029 Peoples R China

ISBN: (纸本)9781665423144

Power electronic circuit parameter identification is the main means to judge the state of the circuit. Through identification, we can know whether there is a fault in the circuit, so the parameter identification of power electronic circuit is very important. However, there are some defects in the previous identification work, which can only work on part of the device, but it is difficult to identify the other part of the device, so the characteristic parameters of this kind of device can not be obtained, which shows that the previous power electronic circuit parameter identification methods need to be optimized. In view of this, the relevant fields put forward the optimization scheme of particle swarm optimization algorithm. It is believed that the algorithm can make up for the defects of previous power electronic circuit parameter identification. This paper will also study this problem, elaborate the specific application mode of the algorithm, and carry out simulation and physical experiments on the application results to verify the effectiveness of the algorithm.

关键词： particle swarm optimization algorithm Power Electronic Circuit Parameter Identification Parameter Identification Pptimization

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Research on Financial Risk Warning Based on Intelligent algorithms 5

Research on Financial Risk Warning Based on Intelligent Algo...

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2024 5th International Conference on Big Data Economy and Information Management, BDEIM 2024

作者： Mou, Yanrong School of Business Wuhan Qingchuan University Hubei Wuhan China

ISBN: (纸本)9798400711862

Financial risk early warning has always been one of the important research topics in corporate management decision-making. Timely discovering the existence of financial risks and resolving or avoiding risks in advance can reduce the probability of enterprises falling into financial crisis and avoid certain investment losses for investors. Aiming at the imbalanced sample characteristics of financial risk early warning scenarios, this paper starts from the perspective of improving sample imbalance and takes the construction of a classification model with good generalization performance as the goal to carry out research on financial risk early warning models. To this end, this study uses the particle swarm algorithm to improve the initial weights and thresholds of the neural network, and then uses the optimal solution obtained by the algorithm as the new weights and thresholds of the network. Finally, the fully trained network is used to predict and simulate the financial risk level of listed companies. The research results show that the prediction accuracy of the financial risk early warning model based on the PSO-BP neural network reaches 86.4%, and through the robustness test of the model, it shows that the financial risk early warning model constructed in this paper is reasonable and has application value;by comparing the prediction results with the early warning models based on BP neural network and GA-BP neural network algorithm, it is found that the PSO-BP neural network early warning model is superior to the other two models and has higher prediction accuracy, which can better predict corporate financial risks. © 2024 Copyright held by the owner/author(s).

关键词： BP neural network Electronic information industry Financial warning particle swarm optimization algorithm

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