检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

870 篇 期刊文献
554 篇 会议
1 篇 学位论文

馆藏范围

1,425 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,300 篇 工学
- 571 篇 计算机科学与技术...
- 382 篇 电气工程
- 256 篇 控制科学与工程
- 160 篇 机械工程
- 134 篇 石油与天然气工程
- 131 篇 信息与通信工程
- 114 篇 软件工程
- 102 篇 材料科学与工程（可...
- 99 篇 动力工程及工程热...
- 76 篇 仪器科学与技术
- 57 篇 电子科学与技术（可...
- 48 篇 环境科学与工程（可...
- 46 篇 力学（可授工学、理...
- 41 篇 土木工程
- 30 篇 化学工程与技术
- 28 篇 交通运输工程
- 19 篇 建筑学
- 18 篇 水利工程
- 18 篇 船舶与海洋工程
- 17 篇 网络空间安全
256 篇 理学
- 92 篇 物理学
- 62 篇 数学
- 51 篇 化学
- 30 篇 生物学
- 29 篇 地球物理学
- 14 篇 系统科学
224 篇 管理学
- 221 篇 管理科学与工程(可...
25 篇 医学
- 15 篇 临床医学
14 篇 经济学
- 12 篇 应用经济学
9 篇 农学
5 篇 法学
4 篇 教育学
1 篇 艺术学

主题

1,425 篇 particle swarm o...
101 篇 genetic algorith...
45 篇 optimization
33 篇 support vector m...
28 篇 multi-objective ...
28 篇 particle swarm o...
26 篇 bp neural networ...
23 篇 fault diagnosis
23 篇 artificial neura...
21 篇 simulated anneal...
20 篇 neural network
17 篇 cloud computing
16 篇 support vector r...
16 篇 task scheduling
14 篇 particle swarm o...
13 篇 convergence
12 篇 differential evo...
12 篇 inertia weight
11 篇 parameter optimi...
11 篇 cuckoo search al...

机构

9 篇 xidian univ sch ...
7 篇 univ chinese aca...
7 篇 china univ geosc...
7 篇 beijing union un...
6 篇 univ sci & techn...
6 篇 natl taiwan univ...
5 篇 shanghai dianji ...
5 篇 chinese acad sci...
5 篇 china univ geosc...
4 篇 northeastern uni...
4 篇 northeastern uni...
4 篇 hubei key lab ad...
4 篇 shanghai jiao to...
4 篇 natl taipei univ...
3 篇 hunan univ sch c...
3 篇 lanzhou univ sch...
3 篇 north china elec...
3 篇 dalhousie univ d...
3 篇 wuhan inst techn...
3 篇 college of mecha...

作者

12 篇 kuo r. j.
9 篇 tian wenjie
8 篇 wu min
8 篇 chen dongdong
8 篇 li di
8 篇 bu yan-ping
8 篇 yang yintang
7 篇 liu jicheng
7 篇 zhou wei
6 篇 fei chunlong
6 篇 wang wei
5 篇 wang ping
5 篇 wang jie-sheng
5 篇 wang jun
4 篇 demidova liliya
4 篇 li liang
4 篇 wang hui
4 篇 feng wei
4 篇 wu lifeng
4 篇 liu tao

语言

1,305 篇 英文
74 篇 其他
34 篇 中文
2 篇 朝鲜文
1 篇 德文

检索条件"主题词=particle swarm optimization algorithm"

共 1425 条记录，以下是911-920 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Iterative PSO algorithms for GRAP Problems

Iterative PSO Algorithms for GRAP Problems

引用

2019 International Conference on Artificial Intelligence, Control and Automation Engineering(AICAE 2019)

作者： Fang LU Dong-kui LI Faculty of Mathematics Science School Baotou Teachers College Editorial Department of Journal Baotou Teachers College

This paper studies the grap problems of two-state, in which the subsystem allows components to be mixed(i.e. the subsystem selects components from several types of heterogeneous components, and the number of selected components types >=1). Each component has a fixed reliability, weight and price, and determines the number of selected components, so that the system has the greatest reliability under the given cost and weight constraints. The coding method of the solution is that the number of elements of each type of subsystem is a variable, and the whole system is arranged in the order of subsystems to form row vectors. An iterative particle swarm optimization algorithm with fixed compression coefficient and dynamic inertia weight is constructed to solve the problem. Typical improved fyffe problems are tested, and the optimal solutions are obtained, which are consistent with the results given by the substitution constraint method. The pso algorithm presented in this paper can effectively solve the grap problem which is allowed to mix components in subsystems.

关键词： Mixed components The reliability redundancy allocation problem particle swarm optimization algorithm Encoding Optimal solution

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting Using Optimized LSTM Networks Based on EMD 10

Short-Term Load Forecasting Using Optimized LSTM Networks Ba...

引用

10th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Li, Tiantian Wang, Bo Zhou, Min Zhang, Lianming Zhao, Xin Nanjing Univ Sch Management & Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538693896

Short-term load forecasting is one of the crucial sections in smart grid. Precise forecasting enables system operators to make reliable unit commitment and power dispatching decisions. With the advent of big data, a number of artificial intelligence techniques have been used to predict the load of the next day. Nevertheless, due to the noise of raw data and the randomness of power load, forecasting errors of existing approaches are relatively large. In this study, a short-term load forecasting method is proposed on the basis of empirical mode decomposition and long short-term memory networks, the parameters of which are optimized by a particle swarm optimization algorithm. Essentially, empirical mode decomposition can decompose the original time series of historical data into relatively stationary components and long short-term memory network is able to emphasize as well as model the timing of data, the joint use of which is expected to effectively apply the characteristics of data itself, so as to improve the predictive accuracy. The effectiveness of this research is exemplified on a realistic data set, the experimental results of which show that the proposed method has higher forecasting accuracy and applicability, as compared with existing methods.

关键词： Short-term load forecasting long short-term memory empirical mode decomposition particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective optimization of Difference Coefficient of Generator Excitation System during Multi-Period

Multi-Objective Optimization of Difference Coefficient of Ge...

引用

China International Conference on Electricity Distribution (CICED)

作者： Wang Ping Fan Lei Liang Ling Chen Feng Wang Guosong Wan Huijiang Zhou Siming Guizhou Power Grid Co Ltd Guiyang 550002 Guizhou Peoples R China NR Elect Co Ltd Nanjing 211102 Jiangsu Peoples R China

ISBN: (纸本)9781538667750

This paper proposes a multi-objective optimization model for the difference coefficient of generator excitation system to improve grid operation voltage level during multi-period and reduce grid loss. The minimum voltage fluctuation at power grid pilot point during multi-period and the minimized active grid loss are taken as the objective functions, while the power flow equations are used as the constraints. The particle swarm optimization (PSO) algorithm is employed to solve the optimization model. The effects of the difference coefficients of generator excitation system on power grid operation voltage are analyzed in detail. The simulation results indicate that the operating voltage level of power grid can be effectively improved and the grid loss can be significantly reduced.

关键词： difference coefficient generator excitation system particle swarm optimization algorithm power grid reactive power and voltage

来源：评论

学校读者我要写书评

暂无评论

A Optimal Path Planning of Multi-destinations for Mobile Robot in Complex Environment

A Optimal Path Planning of Multi-destinations for Mobile Rob...

引用

Chinese Automation Congress (CAC)

作者： Liu, Ping Wang, Chunying Qin, Hongzheng Sun, Jinju Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9781728113128

A lot of progress has been made in the research of point-to-point and complete coverage path planning of mobile robots, while the multi-destinations path planning is seldom reported in the literatures. Based on particle swarm optimization and vortex search algorithms, this paper proposes a multi-destinations path planning approach, which is suited to plan a feasible, safe and optimal path between multi-destinations in complex home environment for mobile robot. Firstly, the sequence of the destinations is quickly optimized by using the particle swarm optimization algorithm. Then, the collision-free path between destinations is obtained via vortex search algorithm with its advantages of high efficiency and small computation. Finally, simulation results show that the proposed multi-destinations path planning approach has the good explorative and exploitation ability, while the path planned by proposed approach is smooth, short.

关键词： Multi-destinations optimization particle swarm optimization algorithm Vortex search algorithm Optimal path planning

来源：评论

学校读者我要写书评

暂无评论

particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

引用

第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

来源：评论

学校读者我要写书评

暂无评论

1st order nonstationary element optimization using PSO algorithm 23

1st order nonstationary element optimization using PSO algor...

引用

23rd International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Wiechetek, Katarzyna Kaszynski, Roman West Pomeranian Univ Technol Fac Elect Engn Szczecin Poland

ISBN: (纸本)9781538643259

The paper presents a new concept of establishing the varying function parameters in order to reduce the duration of the transition processes. The optimization was conducted using particle swarm optimization algorithm (PSO). The main object of the research was 1st order element with two outputs: a low-pass and a high-pass. By selecting the right values of the parameters a better operation and considerably shorter settling times were achieved. After applying multiobjective optimization algorithm the system could adapt to more requirements at once.

关键词： nonstationary filter time-varying parameters transient state multiobjective optimization particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved Optimal Path algorithm Based on Ant Colony algorithm

Improved Optimal Path Algorithm Based on Ant Colony Algorith...

引用

2019 4th International Conference on Automatic Control and Mechatronic Engineering(ACME 2019)

作者： Zhaohua Long Ruifang Dong Chongqing University of Posts and Telecommunications

Ant colony algorithm is based on Ant System(AS) and it is a very important group intelligence algorithm,which is used in many fields,but there are also some *** classical ant colony algorithm is analyzed and studied,and an improved P-ACS algorithm is proposed based on ACS algorithm in this *** analysis and experiment,it is found that although the performance of ACS algorithm is higher than AS algorithm,there are still some problems,such as:falling into local optimal solution,search stagnation,and slow initial *** important reason for the above problems is that the pheromone update can not accurately reflect the actual situation of the *** at this problem,a P-ACS ant colony algorithm is proposed based on particle swarm optimization algorithm(PSO).The algorithm optimizes the pheromone update strategy from three aspects:pheromone concentration range setting,initial pheromone setting and global update strategy improvement.

关键词： Ant colony algorithm ACS particle swarm optimization algorithm P-ACS

来源：评论

学校读者我要写书评

暂无评论

Crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system 20

Crow search algorithm based optimal dynamic performance cont...

引用

20th National Power Systems Conference (NPSC)

作者： Kumar, Sudeep Kumar, Akshay Shankar, Gauri Indian Inst Technol ISM Elect Engn Dept Dhanbad Jharkhand India

ISBN: (纸本)9781538661598

A study on improvement of dynamic as well as steady state performance of power system model using static VAR compensator (SVC) is carried out in this paper. SVC refers to a shunt device within the family of flexible alternating current transmission system and is widely used for enhancing voltage profile and power transmission capability. SVC along with power system stabilizer (PSS) is employed to damp out the oscillation and take back the system to a stable state following disturbance. Crow search algorithm (CSA) is implemented to optimize conventional parameters of PSS and SVC. Simulation results found by it are compared with others optimization algorithms such as particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO). Also, power system model under the effect of CSA based tuned PSS and SVC is verified using eigenvalue analysis under steady state condition for investigating steady state stability. A comparative time domain simulation under MATLAB/SIMULINK platform employing CSA, PSO and TLBO tuned PSS and SVC with studied single machine infinite bus system is carried for dynamic stability assessment. Results obtained confirm the efficacy of the CSA in better tuning of PSS and SVC than other counterparts.

关键词： Crow search algorithm particle swarm optimization algorithm power system stabilizer static VAR compensator teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Lifetime Enhancement of Wireless Sensor Networks by using MFO algorithm

Lifetime Enhancement of Wireless Sensor Networks by using MF...

引用

International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538644911

Wireless sensor networks (WSNs) are extensively used in numerous applications from sensing and tracking to atmospheric quantity measurement. Sensor nodes used in the network are mostly battery powered and due to odd terrain of deployment it is not always easy to replace them. For managing this battery resource of the WSNs various schemes have been proposed and implemented. In this paper we have used Moth Flame optimization algorithm in the clustering and routing for enhancing the lifetime of the sensor network. Outcome through this algorithm are compared with the previously used various algorithms like particle swarm optimization, Genetic algorithm and Least Distance Clustering algorithm.

关键词： WSN Moth flame optimization particle swarm optimization algorithm PSO Genetic algorithm Clustering Routing

来源：评论

学校读者我要写书评

暂无评论

Research on Hospital Operation Index Prediction Method Based on PSO-Holt-Winters Model 18

Research on Hospital Operation Index Prediction Method Based...

引用

2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Zheng, Tao Zhang, Ya Fan, Chenhao Shanghai Jiao Tong Univ Tongren Hosp Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Tongren Hosp Shanghai Peoples R China

ISBN: (纸本)9781450365123

The prediction(1) of hospital operation indicators is of great significance and can provide an important basis for hospital operation and management, so as to assist managers to make decisions such as resource allocation and task planning. In order to solve this problem, a novel Holt-Winters model based on particle swarm optimization (PSO) is proposed, aiming at the accurate prediction of hospital operating indicators. In the process of model construction, according to the characteristics of time series data of hospital operation indicators, a time decay mean square error function is constructed as an optimization function of particle swarm optimization algorithm, which enables particle swarm optimization algorithm to better fit recent historical data and grasp the characteristics of recent time series, so as to improve the prediction accuracy. An example is given to analyze the hospital operation index data of a third-class hospital from 2014 to 2017. By initializing the parameters of the model and optimizing the parameters, the improved PSO-Holt-Winters model of TDMSE-1 is established, which can accurately predict the outpatient, inpatient, emergency, discharged and surgical cases.

关键词： Hospital operation indicators particle swarm optimization algorithm Holt - Winters model

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共143页 << < 88 89 90 91 92 93 94 95 96 97 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：