检索结果-内蒙古大学图书馆

5th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： Kong, Lingshuang Gong, Xiaolong Yuan, Chuanlai Xiao, Huiqin Liu, Jianhua Hunan Univ Technol Sch Elect & Informat Engn Zhuzhou 412007 Peoples R China

In order to improve the prediction accuracy of nonlinear time series, a prediction model based on particle swarm optimization B-spline network is proposed. In designing the structure of the network, the nodes of B-spline basis functions which are considered to be independent variables and every correlative weight parameter are to be optimized together in the network training process. And the forecasting error square sum is adopted to evaluate the training effect of the network. A particle swarm optimization algorithm with an appropriate search strategy is used as the training algorithm to search the distribution of optimal nodes of B-spline basis functions and find the optimal weight parameters, so that the structure of the network is optimized Then, the nonlinear time series is predicted by the network. The simulation results indicate that the prediction model based on particle swarm optimization B-spline network has a fine generalization performance, and the algorithm optimizes the network effectively. The proposed prediction model is not only simple in structure, but also has higher prediction accuracy. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： B-spline networks particle swarm algorithm nonlinear time series prediction model

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Reactive Power Optimization Based on Chaos particle swarm Optimization algorithm

Multi-Objective Reactive Power Optimization Based on Chaos P...

引用

2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

作者： He Xiao Pang Xia Zhu Da-rui Liu Chong-xin Xi An Jiao Tong Univ Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781479927166

Reactive power optimization is closely related to voltage quality, network loss and it has great significance for the safety, reliability and economical operation of the power system. For shortage of traditional reactive power optimization, this paper establishes a multiple-objective reactive power optimization model which consists of minimum active power loss, minimum node voltage deviation, best static voltage stability and minimum reactive cost. To optimize four targets simultaneously, this paper has proposed a multi-objective reactive power optimization method which applies the chaotic particle swarm optimization algorithm based on Pareto solutions and finds the Pareto optimal solution sets of multi-objective optimization problems, then policy makers can make a scientific decision according to the actual situation. To prove the validity of the method proposed, this paper makes a multiple-objective reactive power optimization analysis for the IEEE30-bus system. The result shows that the method presented in this paper can achieve good results of reactive power optimization for decision makers to refer to.

关键词： Reactive power optimization Chaos optimized particle swarm algorithm Pareto solutions Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Annual Runoff Forecast Based on Cooperative particle swarm Projection Pursuit Regression Model

Annual Runoff Forecast Based on Cooperative Particle Swarm P...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Xinxin Li Jing Xu College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

According to the high-dimensional nonlinear problem of annual runoff prediction, to build runoff forecasting model based on projection pursuit regression model of Hermite polynomials and the cooperative particle swarm optimization algorithm. Projection pursuit prediction model projects high-dimensional data into low-dimensional space based on sample data driving, completely according to the sample data driven to enhance the prediction results objectivity. The particle swarm optimization algorithm combines the idea of co-evolution to optimize the projection direction and polynomial coefficients in parallel, and further improve the convergence rate and prediction accuracy of the model. The model is applied to the flow prediction of Jiubujiang River Reservoir. The relative error of runoff prediction is less than 15%, and the prediction result is high precision and reliability. The experimental results show that it is feasible and effective to use the cooperative particle swarm projection pursuit regression model to predict the annual runoff.

关键词： projection pursuit regression model co-evolution algorithm particle swarm algorithm Hermite polynomial annual runoff prediction Hermite polynomials particle swarm algorithm Annual runoff Projection Pursuit Regression particle swarm optimization evolutionary algorithm Data sampling data driven

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence techniques and their applications in drilling fluid engineering: A review

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2018年 167卷 300-315页

作者： Agwu, Okorie E. Akpabio, Julius U. Alabi, Sunday B. Dosunmu, Adewale Univ Uyo Fac Engn Dept Chem & Petr Engn PMB 1017 Uyo Nigeria Univ Port Harcourt Dept Petr Engn Port Harcourt Rivers State Nigeria

For an oil well to be said to have been successfully and conclusively drilled, the drilling fluid lies at the heart of the solution. Therefore, the guarantee to solving issues in oil well drilling is to contrive an optimal drilling fluid. However, there is usually a complex interplay of factors involved during drilling fluid formulation, property determination, its performance in the well and its relationship with other wellbore drilling parameters. This is so because drilling muds exhibit time dependent properties. This time dependency is the direct product of the synergy among the various active additives that make up the mud and the characteristic of each additive especially at downhole conditions where the effects of temperature and pressure are well pronounced. These additives are more often than not diverse in size, chemical activity, density and surface energy. Deriving knowledge from the data from these parameters in order to develop a functional relationship between them is a challenging task requiring advanced modelling techniques as well as human intuition and experience. The dependence on human intuition and on the experiential knowledge of professional mud engineers lays bare the shortcomings of traditional mud design techniques. Artificial intelligence techniques have been shown to alleviate this challenge. Exploiting the abundant literature on the various applications of artificial intelligence in oil and gas operations, several works that show how and what artificial intelligence techniques are used in the drilling fluid industry, and what have been achieved due to their use have been selected. In this paper, a review of existing artificial intelligence techniques and their applications in drilling fluid engineering is given. This paper also dug up and analyzed the strengths and pitfalls of each artificial intelligence technique. The examination of the strengths and deficiencies was done using the following virtues as the basic criteria: robustness a

关键词： Drilling fluid Artificial neural network Fuzzy logic Case based reasoning Hybrid intelligent systems Genetic algorithm particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of Information Gap Decision Theory to the Design of Robust Wide-Area Power System Stabilizers Considering Uncertainties of Wind Power

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2018年第2期9卷 805-817页

作者： Ke, Deping Shen, Feifan Chung, C. Y. Zhang, Chen Xu, Jian Sun, Yuanzhang Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

This paper proposes the application of information gap decision theory (IGDT) to the design of robust wide-area power system stabilizers (WPSSs) with consideration of wind farm (WF) power outputs variations and transmission line outages. According to IGDT, an optimization problem is constructed to tune WPSS parameters. Then, the derived optimal WPSSs can achieve explicit and favorable robustness to ensure the required damping control effects on the interarea oscillations over a maximum variation range of WF steady-state power outputs in normal and emergent operating conditions. Moreover, with the intent of using the excellent global searching capability of particle swarm optimization (PSO), a customized PSO algorithm is proposed to efficiently solve the resulting highly nonlinear programming problem. Finally, simulations are carried out on a modified New England (10-machine 39-bus) system to validate the efficiency of the IGDT-based design method. The derivedWPSSs exhibit expected robustness with respect to the wind power variations and transmission line outages.

关键词： Information gap decision theory wide-area power system stabilizers wind power particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved Krill algorithm for Reservoir Operation

引用

WATER RESOURCES MANAGEMENT 2018年第10期32卷 3353-3372页

作者： Karami, Hojat Mousavi, Sayed Farhad Farzin, Saeed Ehteram, Mohammad Singh, Vijay P. Kisi, Ozgur Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Texas A&M Univ Zachry Dept Civil Engn Dept Biol & Agr Engn 321 Scoates Hall2117 TAMU College Stn TX 77843 USA Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia

Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study introduces an improved version of krill algorithm for reservoir operation. The algorithm is based on adding an onlooker search mechanism to avoid being trapped in local optima and then updating its position. The new krill algorithm is tested using a case study for irrigation management. The computation time is 33 s for the new algorithm but is 54, 59, and 60 s for krill algorithm, particle swarm optimization and genetic algorithm, respectively. Also, the improved krill algorithm can meet 97% of irrigation demands and has the lowest value of vulnerability index among genetic algorithm, particle swarm optimization, and simple krill algorithm. Also, the average solution of improved krill algorithm is close to the global solution. Results indicate that the improved krill algorithm has high potential for application in water resource management.

关键词： Krill algorithm Genetic algorithm particle swarm algorithm Reservoir operation

来源：评论

学校读者我要写书评

暂无评论

Design optimizations of the thermal performance and cost in an air-gap hollow fiber membrane contactor (AHFMC)

引用

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 125卷 1264-1273页

作者： Huang, Si-Min Huang, Wei-Hao Yang, Minlin Ye, Wei-Biao Hong, Yuxiang Yang, Xiaoxi Dongguan Univ Technol Guangdong Prov Key Lab Distributed Energy Syst Dongguan 523808 Peoples R China Xiangtan Univ Sch Mech Engn Dept Proc Equipment & Control Engn Xiangtan 411105 Peoples R China Lishui Univ Dept Chem & Chem Engn Lishui 323000 Peoples R China

An air-gap hollow fiber membrane contactor (AHFMC) has been proposed and applied for fluid heating in the HVAC applications. The water and the liquid desiccant (solution) streams flow inside the neighboring hollow fiber membranes in a cross-flow arrangement. The solution is heated due to the absorption and mixing heats by absorbing the water vapor from the water stream. A mathematical model of the coupled heat and mass transports in the AHFMC is established by converting it into a parallel-plate one. The optimal design of the AHFMC is studied by using the multi-objective particle swarm optimization (MOPSO) algorithm to obtain the maximum thermal performance and minimum total annual cost together with the coupled heat and mass transfer model. A set of multiple optimum solutions, which are named as "Pareto optimal solutions", are then obtained. Influences of geometric variables of the contactor length, width, height, fiber outer diameter and fiber distances in different directions on the heating performance and total annual cost are studied. The optimally geometrical parameters of the AHFMC are obtained using the MOPSO algorithm. These results are useful for the design of the AHFMC based on the desired temperature lift, the contactor constraint, and the available investment in the real applications. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Cross-flow Air-gap hollow fiber membrane contactor Heat and mass transfer Multi-objective optimization particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

引用

Journal of Central South University 2016年第2期23卷 421-429页

作者：武善玉张平李方古锋潘毅 School of Computer Science and Engineering South China University of Technology Department of Computer Science College of Staten Island Department of Computer Science Georgia State University

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

关键词： service-oriented architecture （SOA） cyber physical systems （CPS） multi-task scheduling service allocation multi-objective optimization particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Consensus Clustering Based on particle swarm Optimization algorithm

Consensus Clustering Based on Particle Swarm Optimization Al...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Ahmed. A. A. Esmin Rodrigo A. Coelho Department of Computer Science Federal University of Lavras - UFLA Lavras MG Brazil Department of Computer Science Federal University of Lavras - UFLA Lavras MG Brazil

ISBN: (纸本)9781479906505

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. So far, many contributions have been made to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, the particle swarm Optimization algorithm (PSO) is proposed to solve the consensus clustering problem. We find that the particle swarm clustering algorithm is efficient for this problem. An empirical study compares the accuracy of our proposed algorithms with other consensus clustering methods including voting on five data sets. The experimental results show that the PSO consensus clustering method produces clustering's that are as good as, and often better than, these other methods.

关键词： component particle swarm optimization cluster data consensus and ensemble clustering particle swarm optimization Cluster Analysis Clustering algorithms particle swarm algorithm Ensembles Clustering methods Vote

来源：评论

学校读者我要写书评

暂无评论

A maximum power point tracking method for PV system with improved gravitational search algorithm

引用

APPLIED SOFT COMPUTING 2018年 65卷 333-348页

作者： Li, Ling-Ling Lin, Guo-Qian Tseng, Ming-Lang Tan, Kimhua Lim, Ming K. Hebei Univ Technol Sch Elect Engn Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan Dalian Univ Technol Sch Business Panjin Peoples R China Univ Nottingham Sch Business Nottingham England Chongqing Univ Chongqing Peoples R China

Photovoltaic (PV) system has gradually become research focus in the field of renewable energy power generation, and the output efficiency of PV system is the major concern of researchers. There are obvious non-linear characteristics in the output of PV system, and it will be greatly affected by external environment. For achieving the maximum output power, PV system must operate under the guidance of maximum power point tracking (MPPT) methods The tracking time and accuracy of these methods need to be improved. Therefore, this study contributes to increase output efficiency of PV system by improving the tracking time and accuracy of existing MPPT methods Specifically, a MPPT method with improved gravitational search algorithm (IGSA-MPPT) was proposed. The dynamic weight was added in the change factor of the gravity constant and the related factors of memory and population information exchange were added into the updating formula of particle velocity. IGSA-MPPT not only reduced the tracking time, but also improved the tracking accuracy and mitigated the fluctuations of the reference voltage. Finally, simulation results are compared with the of MPPT methods with particle swarm Optimization (PSO-MPPT) and gravitational search algorithm (GSA-MPPT). The average tracking time of IGSA-MPPT was reduced by 0.023?s and 0.0116s, and the average increase rates of maximum power were increased by 1.7071% and 0.7001% compared with PSO-MPPT and GSA-MPPT. In the simulations of PV system under the varying irradiance and temperature, the tracking speed and tracking accuracy of IGSA-MPPT were higher than those of PSO-MPPT, GSA-MPPT, GWO-MPPT, ICO-MPPT, and FCGSA-MPPT. In summary, IGSA-MPPT has better performance in tracking time and accuracy than other comparison algorithms. It can improve output efficiency of PV system in practical application. (c) 2018 Elsevier B.V. All rights reserved.

关键词： PV system MPPT Partial shading Improved gravitational search algorithm particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：