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A new method of network traffic prediction based on combination model

PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第3期17卷 1075-1090页

作者： Li, Guohao Tian, Zhongda Shenyang Univ Technol Coll Artificial Intelligence Shenyang 110870 Peoples R China

Network traffic has time-varying and nonlinear characteristics, leading to relatively low accuracy of single linear and nonlinear prediction models. Therefore, a combination model prediction method of autoregressive integrated moving average (ARIMA) model and improved particle swarm optimization (IPSO) bi-directional long short-term memory (BiLSTM) is proposed. First, the ARIMA model is used for nonsmooth modeling to obtain linear variation characteristics. Then the ARIMA prediction values are subtracted from the actual data to obtain the residual series. The optimal parameters of the BiLSTM are also optimized by IPSO. Then the optimized BiLSTM is modeled on the residual series to obtain the residual variation characteristics. Finally, the ARIMA linear predictions and the IPSO optimized BiLSTM residual predictions are summed to obtain the final prediction results. The proposed combination model outperforms the comparison model, has smaller prediction errors, and can better characterize the complex features of network traffic.

关键词： Autoregressive integrated moving average Bi-directional long short-term memory network improved particle swarm optimization algorithm Combination prediction

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Damping of Low-Frequency Oscillations using an SVC-Based Supplementary Controller

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第6期8卷 550-556页

作者： Hasanvand, Hamed Zad, Bashir Bakhshideh Mozafari, Babak Feizifar, Behnam Islamic Azad Univ Dept Elect Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Tehran East Branch Tehran Iran

In this paper, the optimal tunning problem of parameters of a conventional lead-lag controller (LLC) and a fuzzy logic controller (FLC) based on the static Var compensator (SVC) is considered. The solution is obtained using an improved particle swarm optimization (IPSO) algorithm. The membership functions and scaling factors of the FLC and LLC parameters are optimized using the above-mentioned technique. The proposed controllers are settled down to an SVC that is installed at the middle of a transmission line connecting a single machine to an infinite bus system. Simulation results show the superiority of the optimized FLC compared to the optimized LLC and also when the SVC is without the supplementary controller under different disturbances and loading conditions. The simulations and analyses are implemented in MATLAB environment. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： low-frequency oscillations static Var compensator lead-lag controller fuzzy logic controller improved particle swarm optimization algorithm

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Direct Trajectory Planning Method Based on IEPSO and Fuzzy Rewards and Punishment Theory for Multi-Degree-of Freedom Manipulators

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IEEE ACCESS 2019年 7卷 20452-20461页

作者： Lv, Xueying Yu, Zhaoxia Liu, Mingyang Zhang, Guanyu Zhang, Liu Jilin Univ Coll Instrumentat & Elect Engn Changchun 130061 Jilin Peoples R China Shanghai Inst Satellite Engn Shanghai 20000 Peoples R China Jilin Univ Natl Engn Res Ctr Geophys Explorat Instruments Changchun 130061 Jilin Peoples R China

Manipulator is a kind of commonly used multi-degree-of-freedom nonlinear system. There is a strong coupling between the links, and the movement of each link constraints and affects each other, which increases the difficulty of motion analysis of the system and reduces its trajectory planning efficiency under specific task targets. To solve this problem, a direct trajectory planning method based on an improved particle swarm optimization (PSO) algorithm, called IEPSO, and the fuzzy rewards and punishment theory is proposed in this paper. First, on the basis of preserving the local search ability of PSO, the global search ability of the population is improved by increasing a population exchange item. At the same time, in order to avoid the population falling into the local optimal value, the last elimination principle is incorporated into the standard PSO algorithm. Second, the fuzzy rewards and punishment theory is introduced to reduce the redundant decoupling operation, which can not only ensure the accuracy of manipulator trajectory planning but also effectively reduce the calculation amount of the trajectory planning for the multi-degree-of-freedom manipulator, to improve the optimization efficiency. Finally, the direct trajectory planning method of the multi-degree-of-freedom manipulator is compared and tested. It can be seen that the efficiency scalar and accuracy of the proposed direct trajectory planning method are significantly higher than those of other optimization methods.

关键词： improved particle swarm optimization algorithm fuzzy rewards and punishment theory trajectory planning multi-degree-of-freedom manipulators

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Wavelet support vector machine-based prediction model of dam deformation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 110卷 412-427页

作者： Su, Huaizhi Li, Xing Yang, Beibei Wen, Zhiping Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Jiangsu Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Jiangsu Peoples R China Natl Engn Res Ctr Water Resources Efficient Utili Nanjing 210098 Jiangsu Peoples R China Nanjing Inst Technol Dept Comp Engn Nanjing 211167 Jiangsu Peoples R China

Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation. Firstly, the chaotic characteristics and the predictable time scale of dam deformation are identified by implementing the phase space reconstruction of observation data series on dam deformation. Secondly, a SVM-based prediction model of dam deformation is proposed. The reconstructed phase space of observed deformation and the Morlet wavelet basis function are selected as the input vector and the kernel function of SVM. Thirdly, the PSO algorithm is improved to implement the parameter optimization of SVM-based prediction model of dam deformation. Finally, the displacement of one actual dam is taken as an example. The results demonstrate the modeling efficiency and forecasting accuracy can be improved. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Dam deformation Characteristics identification Prediction model Wavelet support vector machine improved particle swarm optimization algorithm

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An Integrated Parameter Identification Method of Asynchronous Motor Combined with Adaptive Load Characteristics

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第2期18卷 1041-1051页

作者： Kang, Zhong-Jian Sun, Yi-Sen Liu, Jia-Xuan China Univ Petr East China Sch New Energy Qingdao Peoples R China

The existing asynchronous motor parameter identification methods only identify the parameters of the asynchronous motor itself, ignoring the identification of the parameters of the load carried by the asynchronous motor. This paper proposes an integrated parameter identification method of the asynchronous motor that uses the improved PSO (particle swarm optimization, PSO) and considers the load adaptive characteristics. Compared with the traditional method, this method firstly combines the PSO method with Space Disturbance (SD) to form an improved PSO method, which prevents the PSO from falling into a local optimal state and enhances the global optimization ability of the PSO method. Secondly, according to the characteristics of different loads, a load identification strategy is constructed. This strategy can judge the type of load carried by the asynchronous motor, which reduces the optimization exploration space of the PSO algorithm and accelerates the optimization speed of the PSO algorithm. Finally, according to the identified load types, the improved particle swarm optimization algorithm combined with the spatial disturbance is used to realize the integrated identification of the asynchronous motor and the load parameters. The validity of the algorithm is verified by an example, and the factors affecting the identification accuracy are analyzed.

关键词： Asynchronous motor Parameter identification improved particle swarm optimization algorithm Pump load Constant torque load

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Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2019年第1期41卷 26-46页

作者： Tian, Zhongda Ren, Yi Wang, Gang Shenyang Univ Technol Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

Short-term wind speed prediction is of importance for power grids. It can mitigate the disadvantageous impacts of wind farms on power systems and enhance the competitiveness of wind power in electricity markets. A short-term wind speed prediction model is proposed. Many useless neurons of incremental extreme learning machine have little influences on the final output, at the same time, reduce the efficiency of the algorithm. In order to solve this problem, based on error minimized extreme learning machine, an improved particle swarm optimization algorithm is proposed to decrease the number of useless neurons, achieve the goal of reducing the network complexity and improving the efficiency of the algorithm. The stability and convergence of the algorithm are proved. The actual short-term wind speed time series is used as the research object. Multistep prediction simulation of short-term wind speed is performed out. Compared with the other prediction models, the simulation results show that the prediction model proposed in this paper reduces the training time of the model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability, meanwhile improve the prediction performance indicators.

关键词： Error minimized extreme learning machine improved particle swarm optimization algorithm incremental extreme learning machine prediction short-term wind speed

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Research on Two-Level Energy Optimized Dispatching Strategy of Microgrid Cluster Based on IPSO algorithm

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IEEE ACCESS 2021年 9卷 120492-120501页

作者： Zhang, Zhiyu Wang, Zhijie Cao, Rongbin Zhang, Hongwei Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

This paper considers the economy and stability of the microgrid cluster system, and proposes a two-level energy optimization dispatching strategy for the microgrid cluster, which aims to coordinate the economic benefits and operational risks of the microgrid while reducing the interactive power and fluctuations between the microgrid cluster and the distribution network, and reduce line loss. The first level takes the microgrid as the research object, takes the highest economic benefit and the lowest operational risk as the optimization goals, uses the improved PSO algorithm to solve the problem, and builds the unit risk-economic benefit ratio screening solution set, and then formulates the internal dispatching candidate strategy of the microgrid. The second level is based on the premise that the interactive power between the microgrid cluster and the distribution network is minimized, and the optimal internal dispatching strategy of each microgrid is determined. Then the line loss of the microgrid cluster system is considered, and the microgrid cluster energy complementary scheme is formulated. Finally, a practical example verifies the feasibility and effectiveness of the two-level dispatching strategy and the improved algorithm.

关键词： Microgrids optimization Dispatching Economics Load modeling Linear programming State of charge Microgrid microgrid cluster multi-objective improved particle swarm optimization algorithm two-level optimization model

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Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

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WATER RESOURCES MANAGEMENT 2020年第11期34卷 3515-3531页

作者： Jiang, Yan Bao, Xin Hao, Shaonan Zhao, Hongtao Li, Xuyong Wu, Xianing Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China PowerChina Resources Ltd Beijing 100044 Peoples R China

We have developed a hybrid model that integrates chaos theory and an extreme learning machine with optimal parameters selected using an improved particle swarm optimization (ELM-IPSO) for monthly runoff analysis and prediction. Monthly streamflow data covering a period of 55 years from Daiying hydrological station in the Chaohe River basin in northern China were used for the study. The Lyapunov exponent, the correlation dimension method, and the nonlinear prediction method were used to characterize the streamflow data. With the time series of the reconstructed phase space matrix as input variables, an improved particle swarm optimization was used to improve the performance of the extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly streamflow prediction was obtained. The accuracy of the predictions of the streamflow series (linear correlation coefficient of about 0.89 and efficiency coefficient of about 0.78) indicate the validity of our approach for predicting streamflow dynamics. The developed method had a higher prediction accuracy compared with an auto-regression method, an artificial neural network, an extreme learning machine with genetic algorithm and with PSO algorithm, suggesting that ELM-IPSO is an efficient method for monthly streamflow prediction.

关键词： Streamflow prediction Chaohe River basin Chaotic dynamic characteristics Phase space reconstruction Extreme learning machine improved particle swarm optimization algorithm

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improved PSO algorithm FOR SHAPE AND SIZING optimization OF TRUSS STRUCTURE

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2013年第4期19卷 542-549页

作者： Li, Yancang Peng, Yang Zhou, Shujing Hebei Univ Engn Coll Civil Engn Handan 056038 Peoples R China

In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO) algorithm and provide an effective method for shape and sizing optimization of truss structure, an improved PSO was proposed. The random direction method was employed to produce high-quality initial population, the fuzzy system was applied in the dynamic adaptive adjustment of parameters of the PSO, and the Metropolis criteria were used to improve the performance of PSO. Then, the improved PSO was introduced to the truss structure shape and sizing optimization design. Engineering practice and comparison with the other optimization algorithms show that the algorithm has good convergence and global searching capability. The study provides a promising algorithm for the structural optimization.

关键词： structural optimization improved particle swarm optimization algorithm truss structure shape optimization sizing optimization

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Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第11期14卷 1639-1651页

作者： Zhang, Huiming Watada, Junzo Wang, Bo Xi An Jiao Tong Univ Sch Econ & Finance 74 West Yanta Rd Xian 710061 Shaanxi Peoples R China Waseda Univ Informat Prod & Syst Res Ctr 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan Univ Teknol PETRONAS Comp & Informat Sci Dept Seri Iskandar 32610 Perak Darul Rid Malaysia Nanjing Univ Sch Management & Engn Nanjing 210093 Jiangsu Peoples R China

To quantitatively discuss the effects and uncertainties of yield changes in each stock for portfolio selection results and then to provide a more reliable portfolio solution for investors, sensitivity analysis is introduced to improve the multi-objective portfolio model with fuzzy VaR (SA-VaR-FMOPSM). Compared with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM), the calculation formulas of expected and VaR value of parabolic fuzzy variable are derived when stock yields are set as a more generalized parabolic fuzzy variable as well as the sensitivity of the total objective to the VaR-FMOPSM model is defined. Meanwhile, based on the fuzzy simulation technique, the model adapted to a series of different distributed fuzzy variable, an improved particle swarm optimization algorithm (IPSO) is used for numerical simulations. To illustrate the proposed model, New York Stock Exchange and National Association of Securities Dealers Automated Quotations-Global Select Market (NASDAQ-GS) stock data were selected for empirical testing, and to better reflect the true value of each stock for each day, we selected the ex-right price data in the experiment, a comparison with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM) is performed. The results show that the fuzzy VaR multi-objective portfolio model based on the sensitivity analysis method can effectively identify and quantitatively analyze the sensitivity of individual stocks to yield changes, i.e. that can identify which stock has more stable yields and that can calculate the degree of stability, then to obtain stable portfolio solutions. In addition, compared with the VaR-FMOPSM model, our sensitivity-based improved model with the IPSO algorithm also performs better than Genetic algorithm and Simulate Anneal algorithm (SA), it provides the same performance on this point. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： fuzzy variables sensitivity analysis fuzzy VaR portfolio model improved particle swarm optimization algorithm

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