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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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Research on gateway deployment of WMN based on maximum coupling subgraph and PSO algorithm

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SOFT COMPUTING 2017年第4期21卷 923-933页

作者： Li, Yang Huang, Shuqiang Fan, Rensheng Zhang, Zhen Zhou, Yuyu Jinan Univ Coll Informat Sci & Technol Guangzhou Guangdong Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Guangdong Peoples R China

The maximum distance from the access points to the nearest gateways determines the network time delay, and has an important effect on network performance in wireless mesh networks. Motivated by the gateway deployment problem, this study is focused on optimizing the gateway deployment by minimizing the maximum distance. This is done by first improving upon theorems so that the plane can be divided into several intersecting regions;vertices locate in the same region are equivalent and can connect the same access points;the coordinates of the regions can also be determined. Then, maximum coupling subgraph is used in order to recognize the maximum intersecting regions;meanwhile, the coordinates are calculated by representative points. Lastly, an RPSO algorithm is designed in which representative points are taken as the initial particles to search the optimal gateway deployment. The simulation results demonstrate that the optimal gateway deployment, as determined by the RPSO algorithm process, has a smaller coverage radius, a more stable result and a faster convergence rate as compared to other algorithms.

关键词： Wireless mesh network Gateway deployment Geometric K-center problem particle swarm optimization algorithm Maximum coupling subgraph Representative point

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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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Lower bound estimation of the maximum allowable initial error and its numerical calculation

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Atmospheric and Oceanic Science Letters 2018年第5期11卷 438-443页

作者： CAO Yi-Xing ZHENG Qin YAN Jun Graduate School PLA Army Engineering University Department of Basic PLA Army Engineering University

In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s *** to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial *** at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it ***,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also ***,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.

关键词： Predictability problem maximum allowable initial error particle swarm optimization algorithm Conditional Nonlinear Optimal Perturbation(CNOP)

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Super-Resolution Reconstruction From Multiple Defocused Infrared Images of Stationary Scene

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2017年第5期9卷 1-16页

作者： Mao, Yuxing Zhao, Benjiang Yan, Dongmei Jia, Haiwei Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Hangzhou Yuanzhou Med Technol Co Ltd Hangzhou 310052 Zhejiang Peoples R China

Infrared image has poor visual effect for its low resolution. Super-resolution reconstruction (SRR) is an effective means to address this problem. Existing SRR algorithms use well-focused images and ignore the value of defocused images generated by the infrared imaging system during focusing. The basic idea of the present study is to treat a defocused infrared image as distribution and accumulation of scene information among different pixels of the infrared detector, as well as a valid observation of the imaged subject;defocused images are the result of blurring a corresponding high resolution (HR) image using a point spread function (PSF) followed by downsampling. From this idea, we used multiple defocused images to build an observation model for HR images and propose an SRR algorithm to approach the HR images. We have developed an image degradation model by analyzing optical lens imaging, using the particle swarm optimization algorithm to estimate the PSF of the HR image, and using compressed sensing theory to implement SRR based on the noncoherent characteristics of the defocused infrared images. Experiments demonstrate that our method can be used to obtain more information about details of a scene and improve the visual effect without adding any hardware facilities, improving the recognition and interpretation of the image subject.

关键词： Defocused infrared image super-resolution reconstruction point spread function compressed sensing particle swarm optimization algorithm

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Simultaneous allocation of electric vehicles' parking lots and distributed renewable resources in smart power distribution networks

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SUSTAINABLE CITIES AND SOCIETY 2017年 28卷 332-342页

作者： Amini, M. Hadi Moghaddam, Mohsen Parsa Karabasoglu, Orkun Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Sun Yat Sen Univ Carnegie Mellon Univ Joint Inst Guangzhou 510006 Guangdong Peoples R China SYSU Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

Electric vehicles (EVs) and distributed renewable resources (DRRs) are introduced to achieve three of the most pivotal objectives of this century: using environmentally-friendly energy resources, reliable supply of the load demand, and sustainable development of power systems. To achieve the aforementioned goals, simultaneous utilization of DRRs and EVs should be implemented in a scheduled manner. In this paper, we propose a two-stage approach for allocation of EV parking lots and DRRs in power distribution network. Our method considers both the economical benefits of parking lot investor and the technical constraints of distribution network operator. First, the parking lot investor offers the candidate buses for installing the parking lot to the distribution network operator based on economic objectives. Then, the distribution network decision-making is obtained to reduce loss of system. The proposed framework not only improves the distribution network loss, but also ameliorates the availability of the parking lot from the economical point of view. In order to solve the formulated optimization problem, we utilize two optimization techniques. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used for the distribution network loss minimization purpose. Besides, we model the EV parking lot by expanding single EV probabilistic model. The performance of the proposed method is evaluated by allocating DRRs and EV parking lots simultaneously on the IEEE standard distribution test system. This system is bus 2 of Roy Billinton Test System (RBTS). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed renewable resource Electric vehicle Power distribution network Parking lot investor Genetic algorithm particle swarm optimization algorithm Multi-objective optimization mart distribution network

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Implementation of Uniform Deformation Theory in semi-active control of structures using fuzzy controller

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SMART STRUCTURES AND SYSTEMS 2017年第4期19卷 351-360页

作者： Mohammadi, Reza Karami Haghighipour, Fariba KN Toosi Univ Technol Dept Civil Engn 1346 Vali Asr Str Tehran Iran

Protection of structures against natural hazards such as earthquakes has always been a major concern. Semi-active control combines the reliability of passive control and versatility and adaptability of active control. So it has recently become a preferred control method. This paper proposes an algorithm based on Uniform Deformation Theory to mitigate vulnerable buildings using magneto-rheological (MR) damper. Due to the successful performance of fuzzy logic in control of systems and its simplicity and intrinsically robustness, it is used here to regulate MR dampers. The particle swarm optimization (PSO) algorithm is also used as an adaptive method to develop a fuzzy control algorithm that is able to create uniform inter-story drifts. Results show that the proposed algorithm exhibited a desirable performance in reducing both linear and nonlinear seismic responses of structures. Performance of the presented method is indicated in compare with passive-on and passive-off control algorithms.

关键词： semi-active control fuzzy system MR damper Uniform Deformation Theory particle swarm optimization algorithm

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