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

597 篇 期刊文献
237 篇 会议
1 篇 学位论文

馆藏范围

835 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

778 篇 工学
- 450 篇 计算机科学与技术...
- 284 篇 电气工程
- 111 篇 控制科学与工程
- 81 篇 信息与通信工程
- 69 篇 石油与天然气工程
- 56 篇 软件工程
- 38 篇 机械工程
- 38 篇 动力工程及工程热...
- 35 篇 电子科学与技术（可...
- 21 篇 力学（可授工学、理...
- 21 篇 仪器科学与技术
- 21 篇 土木工程
- 20 篇 环境科学与工程（可...
- 19 篇 材料科学与工程（可...
- 15 篇 水利工程
- 14 篇 化学工程与技术
- 9 篇 建筑学
- 9 篇 交通运输工程
- 6 篇 航空宇航科学与技...
- 6 篇 网络空间安全
112 篇 理学
- 38 篇 数学
- 34 篇 物理学
- 10 篇 生物学
- 8 篇 化学
- 8 篇 系统科学
- 7 篇 地球物理学
103 篇 管理学
- 99 篇 管理科学与工程(可...
- 6 篇 工商管理
19 篇 医学
- 14 篇 临床医学
7 篇 经济学
- 5 篇 应用经济学
4 篇 教育学
4 篇 农学
2 篇 法学
2 篇 艺术学
1 篇 文学

主题

835 篇 gravitational se...
114 篇 particle swarm o...
77 篇 optimization
34 篇 genetic algorith...
32 篇 swarm intelligen...
27 篇 feature selectio...
24 篇 clustering
23 篇 gsa
18 篇 classification
17 篇 differential evo...
14 篇 image segmentati...
13 篇 artificial bee c...
13 篇 pso
13 篇 search problems
12 篇 multi-objective ...
12 篇 quantum computin...
12 篇 neural network
12 篇 extreme learning...
12 篇 law of gravity
10 篇 firefly algorith...

机构

25 篇 shahid bahonar u...
20 篇 huazhong univ sc...
9 篇 univ toyama fac ...
6 篇 indian sch mines...
5 篇 jaypee inst info...
5 篇 dr bc roy engn c...
5 篇 natl inst techno...
5 篇 china univ petr ...
5 篇 indian inst tech...
5 篇 taiyuan univ tec...
4 篇 ningbo univ fac ...
4 篇 adekunle ajasin ...
4 篇 grad univ adv te...
4 篇 rajasthan tech u...
4 篇 higher educ comp...
4 篇 anqing normal co...
4 篇 tijuana inst tec...
4 篇 asansol engn col...
4 篇 indian inst tech...
3 篇 beijing inst tec...

作者

26 篇 nezamabadi-pour ...
14 篇 rashedi esmat
14 篇 gao shangce
12 篇 han xiaohong
10 篇 zhang aizhu
9 篇 sun genyun
9 篇 deep kusum
9 篇 li chaoshun
8 篇 owolabi taoreed ...
8 篇 xiong xiaoyan
7 篇 mittal himanshu
7 篇 ibrahim zuwairie
7 篇 singh amarjeet
6 篇 rawat tarun kuma...
6 篇 sharma veena
6 篇 mirhosseini mina
6 篇 radosavljevic jo...
6 篇 naresh r.
6 篇 yuan xiaohui
6 篇 valdez fevrier

语言

800 篇 英文
21 篇 其他
9 篇 中文
6 篇 德文
6 篇 法文
3 篇 土耳其文

检索条件"主题词=Gravitational Search algorithm"

共 835 条记录，以下是291-300 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：

Linear Dipole Antenna Array Design and Optimization Using gravitational search algorithm 2

Linear Dipole Antenna Array Design and Optimization Using Gr...

引用

2nd IEEE International Conference On Advances in Electrical and Electronics, Information, Communication and Bio Informatics (IEEE AEEICB)

作者： Swain, Prativa Mohanty, Saumendra Kumar Mangaraj, Biswa Binayak Veer SurendraSai Univ Technol Dept Elect & Telecommun Engn Sambalpur 768018 Odisha India

ISBN: (纸本)9781467397452

This paper presents an application of nature inspired population based algorithm namely gravitational search algorithm (GSA) to Linear Dipole Antenna Array (LDAA) optimization. The design parameters of the LDAA to be optimized are the length of the dipole antenna elements and spacings out of each of the group of two neighbor elements. The self developed GSA using MATLAB is used to optimize the design parameters of the LDAA to determine a set of performance parameters of LDAA such as directivity and side lobe level. The major goal of antenna design is to achieve narrow beamwidth (high directive gain) and low side lobe level. The optimized radiation patterns indicate that the application of the GSA to our optimization problem is found to be a promising one for obtaining a higher directivity and lower side lobe level.

关键词： Directivity gravitational search algorithm Linear Dipole Antenna Array optimization side lobe level

来源：评论

学校读者我要写书评

暂无评论

Hybrid gravitational search algorithm with Swarm Intelligence for Object Tracking 1

引用

23rd International Conference on Neural Information Processing (ICONIP)

作者： Yeung, Henry Wing Fung Liu, Guang Chung, Yuk Ying Liu, Eric Yeh, Wei-Chang Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Natl Tsing Hua Univ Dept Ind Engn & Engn Management POB 24-60 Hsinchu 300 Taiwan

ISBN: (数字)9783319466873

ISBN: (纸本)9783319466873;9783319466866

This paper proposes a new approach to object tracking using the Hybrid gravitational search algorithm (HGSA). HGSA introduces the gravitational search algorithm (GSA) to the field of object tracking by incorporating Particle Swarm Optimization (PSO) using a novel weight function that elegantly combines GSA's gravitational update component with the cognitive and social components of PSO. The hybridized algorithm acquires PSO's exploitation of past information and fast convergence property while retaining GSA's capability in fully utilizing all current information. The proposed framework is compared against standard natural phenomena based algorithms and Particle Filter. Experiment results show that HGSA largely reduces convergence to local optimum and significantly out-performed the standard PSO algorithm, the standard GSA and Particle Filter in terms of tracking accuracy and stability under occlusion and non-linear movement in a large search space.

关键词： Object tracking gravitational search algorithm Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Clustering for Wireless Sensor Networks: A gravitational search algorithm 6th

Energy Efficient Clustering for Wireless Sensor Networks: A ...

引用

6th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Rao, P. C. Srinivasa Banka, Haider Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9783319489599;9783319489582

Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper, a gravitational search algorithm (GSA) based approach has been presented called GSA-EEC (GSA based Energy Efficient Clustering). The algorithm is designed with an efficient encoding scheme of an and a new fitness function. For the efficient design of WSNs. we consider the Euclidian distance from the sensors to gateways and gateways to sink and residual energy of gateways. The GSA-EEC is simulated extensively with varying number of sensor and gateways and various scenarios of WSNs. To show the efficacy of the GSA-EEC, we compared with some of the benchmark clustering algorithms.

关键词： Wireless sensor networks Clustering NP-hard gravitational search algorithm Network life time

来源：评论

学校读者我要写书评

暂无评论

Optimal StatCom Placement using gravitational search algorithm 1

Optimal StatCom Placement using Gravitational Search Algorit...

引用

1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Rana, Bharat Singh Srivastava, Laxmi Madhav Inst Sci & Technol Elect Engn Dept Gwalior India

ISBN: (纸本)9781467385879

A nature inspired evolutionary computing approach called, gravitational search algorithm (GSA) has been proposed for optimal placement of StatCom, to maximize the voltage stability margin and also improve voltage profile by minimizing the total voltage deviation in the power system. The proposed nature inspired evolutionary computing approach has been performed using two steps. As the first step, the weakest buses selected for placement of StatCom using modal analysis. In the second step GSA is applied, to select the optimal allocation of StatCom considering these selected weakest buses. Fuzzy performance Index has been used, for combining the various objectives which are conflicting in nature. The proposed nature inspired computing technique (GSA) has been tested on the standard IEEE 14-bus system considering the stressed condition as well as contingencies during stressed condition. To establish the superiority of proposed GSA approach, the results using Genetic algorithm (GA) were compared the performance of GSA. On the basis of comparison, GSA has been found to be superior to GA with respect to it convergence characteristic and accuracy as well.

关键词： Fuzzy Logic gravitational search algorithm Genetic algorithm StatCom Voltage Stability Margin

来源：评论

学校读者我要写书评

暂无评论

Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm 6

Feature weighting for cancer tumor detection in mammography ...

引用

6th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Shirazi, Fatemeh Rashedi, Esmat Grad Univ Adv Technol Dept Elect Engn Kerman Iran

ISBN: (纸本)9781509035861

Optimization methods have been widely used in image processing and computer vision. In this paper, k-nearest neighbor (KNN) and real-valued gravitational search algorithm (RGSA) are used to detect the breast cancer tumors in mammography images. GSA is used as a tool for optimization of the features weighting (FW) and tuning the classifier. FW-KNN based on GSA is employed to enhance the K-NN classification accuracy. The weighted features and the tuned K-NN classifier are utilized for detecting tumors. The obtained results show good efficiency of GSA-based FW-KNN classification for breast cancer tumor detection.

关键词： breast cancer detection k-nearest neighbor classification feature weighting gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Economic load dispatch using improved gravitational search algorithm 2

Economic load dispatch using improved gravitational search a...

引用

2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS)

作者： Huang Yu Wang Jia-rong Guo Feng Tsinghua Univ Dept Thermal Engn State Key Lab Power Syst Beijing 100084 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Baoding 071003 Peoples R China North China Elect Power Univ Hebei Engn Res Ctr Simulat & Optimized Control Po Baoding Peoples R China Xiamen Univ Sch Informat Sci & Engn Dept Cognit Sci Xiamen Peoples R China

ISBN: (纸本)9781510601543

This paper presents an improved gravitational search algorithm(IGSA) to solve the economic load dispatch(ELD) problem. In order to avoid the local optimum phenomenon, mutation processing is applied to the GSA. The IGSA is applied to solve the economic load dispatch problems with the valve point effects, which has 13 generators and a load demand of 2520 MW. Calculation results show that the algorithm in this paper can deal with the ELD problems with high stability.

关键词： economic load dispatch gravitational search algorithm mutation operation

来源：评论

学校读者我要写书评

暂无评论

Remote sensing imagery classification using multi-objective gravitational search algorithm 22

Remote sensing imagery classification using multi-objective ...

引用

Conference on Image and Signal Processing for Remote Sensing XXII

作者： Zhang, Aizhu Sun, Genyun Wang, Zhenjie China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China Heriot Watt Univ Inst Petr Engn Edinburgh EH14 4AS Midlothian Scotland

ISBN: (数字)9781510604131

ISBN: (纸本)9781510604124;9781510604131

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

关键词： Multi-objective optimization remote sensing cluster classification gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Forecasting Energy Consumption using Particle Swarm Optimization and gravitational search algorithm

Forecasting Energy Consumption using Particle Swarm Optimiza...

引用

International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

作者： Manjhi, Yogesh Dhar, Joydip ABV Indian Inst Informat Technol & Management Gwalior 474015 Madhya Pradesh India

ISBN: (纸本)9781467395458

A hybrid algorithm for short-term load forecasting is proposed. The particle swarm optimization algorithm used in the training phase of the artificial neural network is optimized by combining it with the gravitational search algorithm. In this paper, we have combined the exploitation of PSO and exploration of GSA to form a single algorithm that can be used to get more accurate results for load forecast. The results reflect that hybrid algorithm avoids local minimum and have better convergence speed than the PSO algorithm and GSA algorithm individually.

关键词： Load forecasting multilayer perceptron neural networks particle swarm optimization gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Neural Network Classification for Iris Recognition using both Particle Swarm Optimization and gravitational search algorithm

Neural Network Classification for Iris Recognition using bot...

引用

World Symposium on Computer Applications and Research (WSCAR)

作者： Rizk, Mohamed R. M. Farag, Hania H. A. Said, Lamiaa A. A. Alexandria Univ Engn Dept Elect Engn Alexandria Egypt

ISBN: (纸本)9781509041145

This paper introduces an iris classification system using FFNNGSA and FFNNPSO. The use of both methods has not been done before in iris recognition. This iris identification system consists of localization of the iris region, normalization, feature extraction and then classification as a final stage. A Canny Edge Detection scheme and a Circular Hough Transform are used to detect the iris boundaries. After that the extracted IRIS region is normalized using Daugman rubber sheet model. Next, Haar wavelet transform is used for extracting features from the normalized iris region then the feature matrix is reduced using the principle component analysis (PCA). Finally, both particle swarm optimization (PSO) and gravitational search algorithm (GSA) are used for training a forward neural network to get the optimum weights and biases that give minimum error and higher recognition rate for the FFNN in iris classification. These optimization techniques used in classification strengthen the work. The results showed that training the feedforward neural network by GSA is better than training it by PSO in an iris recognition system.

关键词： Canny Edge Detection Circular Hough transform Normalization Particle swarm optimization gravitational search algorithm Principle component analysis

来源：评论

学校读者我要写书评

暂无评论

Fitness Based gravitational search algorithm

Fitness Based Gravitational Search Algorithm

引用

IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Gupta, Aditi Sharma, Nirmala Sharma, Harish Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509016662

gravitational search algorithm (GSA) is a swarm intelligence based optimization algorithm which is based on the law of gravity and the law of motion of mass interaction between individuals. In GSA, the solution search process depends on the velocity which is a function of acceleration and the previous velocity. In the solution search process, acceleration plays important role and depends on the masses and forces of the individuals. Due to this component, GSA some times slow in convergence, while some time prematurely converge to the local optima. To avoid this situation, a new velocity update strategy is proposed, in which new velocity depends on the previous velocity and the acceleration, based on the fitness of the solutions. The proposed strategy is named as fitness based gravitational search algorithm (FBGSA). In FBGSA, the high fit solutions are motivated to exploit the promising search regions, while the low fit solutions have to explore the search space. Further, performance of the proposed strategy is compared with basic GSA and another swarm intelligence based algorithm, namely biogeography based optimization (BBO) algorithm over 16 different benchmark functions. Reported results show that FBGSA is a competitive variant of GSA algorithm.

关键词： Swarm intelligence gravitational search algorithm Optimization Nature inspired algorithm

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84页 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：