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Integrated parallel forecasting model based on modified fuzzy time series and SVM

Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China China Defense Science & Technology Information Center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy C-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

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Fast basis search for adaptive Fourier decomposition

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1页

作者： Wang, Ze Wan, Feng Wong, Chi Man Qian, Tao Univ Macau Dept Elect & Comp Engn Fac Sci & Technol Ave Univ Taipa Macau Peoples R China Univ Macau Dept Math Fac Sci & Technol Ave Univ Taipa Macau Peoples R China

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

关键词： Adaptive Fourier decomposition Unscented Kalman filter Nelder-Mead algorithm Genetic algorithm particle swarm optimization algorithm

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Prediction of Novel High-Pressure Structures of Magnesium Niobium Dihydride

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ACS APPLIED MATERIALS & INTERFACES 2017年第31期9卷 26169-26176页

作者： Zhang, Chuanzhao Sun, Guoliang Wang, Jingjing Lu, Cheng Jin, Yuanyuan Kuang, Xiaoyu Hermann, Andreas Sichuan Univ Inst Atom & Mol Phys Chengdu 610065 Sichuan Peoples R China Nanyang Normal Univ Dept Phys Nanyang 473061 Peoples R China Yangtze Univ Dept Phys & Optoelect Engn Jingzhou 434023 Peoples R China Univ Nevada Dept Phys Las Vegas NV 89154 USA Univ Nevada High Pressure Sci & Engn Ctr Las Vegas NV 89154 USA Univ Edinburgh Sch Phys & Astron Ctr Sci Extreme Condit Edinburgh EH9 3JZ Midlothian Scotland Univ Edinburgh Sch Phys & Astron SUPA Edinburgh EH9 3JZ Midlothian Scotland

On the basis of a combination of the particle-swarm optimization technique and density functional theory (DFT), we explore the crystal structures of MgH2, NbH2, and MgNbH2 under high pressure. The enthalpy-pressure (H-P) diagrams indicate that the structural transition sequence of MgH2 isay alpha -> gamma -> delta -> epsilon -> zeta and that NbH2 transforms from the Fm (3) over barm phase to the Pnma phase at 47.80 GPa. However, MgNbH2 is unstable when the pressure is too low or too high. Two novel MgNbH2 structures, the hexagonal P (6) over bar m2 phase and the orthorhombic Cmcm phase, are discovered, which are stable in the pressure ranges of 13.24-128.27 GPa and 128.27-186.77 GPa, respectively. The P (6) over bar m2 phase of MgNbH2 consists of alternate layers of polymetric NbH6 and MgH6 triangular prisms, while the Cmcm phase contains distorted MgH6 trigonal prisms. The calculated elastic constants and phonon dispersions confirm that both phases are mechanically and dynamically stable. The analyses of density of states (DOS), electron localization function (ELF), and Bader charge demonstrate that a combination of ionic and metallic bonds exist in both P (6) over bar m2 and Cmcm phases. We hope the newly predicted magnesium niobium dihydrides with desirable electronic properties will promote future experimental and theoretical studies on mixed main group-transition metal hydrides.

关键词： magnesium niobium dihydride particle swarm optimization algorithm density functional theory structural transition electronic structures

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A MODIFIED particle swarm optimization TO IMPROVE PRECISION OF COMPLEX ASSEMBLY

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2016年第4期31卷 329-340页

作者： Kumar, Ponnaiah Peter Anand Maheswaran, Rathinam Raj, Mathana Singh Victor VV Coll Engn Fac Mech Engn Dept Tisaiyanvilai Tamil Nadu India Mepco Schlenk Engn Coll Fac Mech Engn Dept Sivakasi Tamil Nadu India Sardar Raja Coll Engn Fac Mech Engn Dept Alangulam Tamil Nadu India

An assembly consists of two or more mating parts. The quality of the assembly is mainly based on the quality of mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. To obtain high precision assemblies, clearance variation has to be reduced. Selective assembly helps to reduce this clearance variation. Selective assembly is a method of obtaining high precision assembly from relatively low precision components. In selective assembly, the mating parts are manufactured with wide tolerances. The mating part population is then partitioned to form selective groups, and the appropriate selective groups are assembled interchangeably. This paper presents a modified particle swarm optimization (MPSO) to improve the precision of a complex assembly which consists of piston, piston ring and cylinder. The proposed MPSO is based on the concept that an individual in the PSO not only learns from its best experience, but also learns from its mistakes referred as personal worst position. The idea of this modification is based on the social behaviour that each particle tries to leave its previous worst position. The main distinction of this approach is in using particle's worst (but worth) experience instead of the best previous experience. The results show that the proposed approach is more apt to find the global optima of the problem.

关键词： Complex assembly precision particle swarm optimization algorithm selective assembly

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Energy-Storage optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

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ENERGIES 2018年第12期11卷 3393-3393页

作者： Yun, Pingping Ren, Yongfeng Xue, Yu Inner Mongolia Univ Technol Coll Energy & Power Engn Hohhot 010051 Inner Mongolia Peoples R China Inner Mongolia Engn Res Ctr Wind Power Technol & Hohhot 010051 Inner Mongolia Peoples R China

Wind power penetration ratios of power grids have increased in recent years;thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

关键词： wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level

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A self-adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2017年第18期40卷 7843-7857页

作者： Zeng, Bo Liu, Sifeng Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Jiangsu Peoples R China

The self-adaptive intelligence gray predictive model (SAIGM) has an alterable-flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the particle swarm optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.

关键词： fractional order accumulating operator particle swarm optimization algorithm prediction of China's electricity consumption self-adaptive intelligence gray predictive model

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A kernel extreme learning machine algorithm based on improved particle swam optimization

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MEMETIC COMPUTING 2017年第2期9卷 121-128页

作者： Lu, Huijuan Du, Bangjun Liu, Jinyong Xia, Haixia Yeap, Wai K. China Jiliang Univ Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China Ctrip Travel Network Shanghai 200335 Peoples R China Zhejiang Sci Tech Univ Coll Informat Hangzhou 310018 Zhejiang Peoples R China Auckland Univ Technol Ctr Artificial Intelligence Res Auckland New Zealand

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.

关键词： Kernel extreme learning machine Active operator particle swarm optimization algorithm Gene expression data classification

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A Novel Flood Forecasting Method Based on Initial State Variable Correction

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WATER 2018年第1期10卷 12页

作者： Li, Kuang Kan, Guangyuan Ding, Liuqian Dong, Qianjin Liu, Kexin Liang, Lili Minist Water Resources China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Beijing IWHR Technol Co Ltd Beijing 100038 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

关键词： flood forecasting forecast accuracy conceptual hydrological model initial state variable particle swarm optimization algorithm

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Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm

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SOFT COMPUTING 2017年第14期21卷 3849-3858页

作者： Kawaria, Narendra Patidar, Rohan George, Nithin V. Indian Inst Technol Gandhinagar Gandhinagar India

Parameter identification of bilinear systems has been considered as an evolutionary computing algorithm-based optimization problem in this paper. A new Levy shuffled frog leaping algorithm (LSFLA), which is an improved version of the conventional shuffled frog leaping algorithm (SFLA), has been designed and has been applied for this parameter identification task. LSFLA offers enhanced local search behaviour in comparison with other traditional evolutionary computing algorithms. The ability of the new algorithm in accurately modeling parameters in single input single output (SISO) as well as multiple input multiple output (MIMO) has been checked using an extensive simulation study. The parameter estimation efficiency of the new scheme has been compared with that obtained using other popular evolutionary computing algorithms and the simulation study reveals the enhanced parameter identification ability of the proposed LSFLA.

关键词： Bilinear system System identification particle swarm optimization algorithm Cuckoo search algorithm Shuffled frog leaping algorithm

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An improved DV-Hop localization algorithm based on evolutionary algorithms

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TELECOMMUNICATION SYSTEMS 2017年第4期64卷 639-647页

作者： Mehrabi, Mahshid Taheri, Hassan Taghdiri, Pooria Amirkabir Univ Technol Dept Elect Engn Switching & Network Lab Tehran Iran Amirkabir Univ Technol Photon Engn Grp Tehran Iran

Localization is one of the most important issues in wireless sensor networks and designing accurate localization algorithms is a common challenge in recent researches. Among all localization algorithms, DV-Hop attracts more attention due to its simplicity;so, we use it as a basis for our localization algorithm in order to improve accuracy. The various evolutionary algorithms such as Genetic, Shuffled Frog Leaping and particle swarm optimization are employed in different phases of the main DV-Hop localization algorithm. Simulation results prove that our proposed method decreases the localization error efficiently without additional hardware.

关键词： Localization Wireless sensor networks DV-Hop Genetic algorithm Shuffled frog leaping algorithm particle swarm optimization algorithm

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