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

146 篇 期刊文献
62 篇 会议
1 篇 学位论文

馆藏范围

209 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

193 篇 工学
- 101 篇 计算机科学与技术...
- 49 篇 电气工程
- 23 篇 控制科学与工程
- 21 篇 机械工程
- 20 篇 软件工程
- 17 篇 材料科学与工程（可...
- 16 篇 信息与通信工程
- 13 篇 土木工程
- 11 篇 石油与天然气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 环境科学与工程（可...
- 7 篇 水利工程
- 6 篇 力学（可授工学、理...
- 6 篇 动力工程及工程热...
- 6 篇 船舶与海洋工程
- 4 篇 仪器科学与技术
- 4 篇 建筑学
- 4 篇 核科学与技术
- 3 篇 生物医学工程（可授...
38 篇 理学
- 15 篇 化学
- 11 篇 物理学
- 6 篇 数学
- 5 篇 生物学
- 4 篇 地球物理学
- 2 篇 大气科学
- 2 篇 海洋科学
36 篇 管理学
- 34 篇 管理科学与工程(可...
12 篇 医学
- 7 篇 临床医学
- 6 篇 基础医学(可授医学...
2 篇 农学
1 篇 经济学
1 篇 教育学

主题

209 篇 back-propagation...
36 篇 neural network
32 篇 artificial neura...
21 篇 neural networks
19 篇 artificial neura...
8 篇 artificial neura...
8 篇 genetic algorith...
6 篇 machine learning
6 篇 backpropagation
4 篇 prediction
4 篇 disturbances
4 篇 feed-forward neu...
4 篇 extreme learning...
4 篇 extended kalman ...
4 篇 adaptive control
3 篇 fault diagnosis
3 篇 conjugate gradie...
3 篇 back propagation
3 篇 convergence
3 篇 rbf neural netwo...

机构

2 篇 univ adelaide de...
2 篇 indian inst tech...
2 篇 univ kebangsaan ...
2 篇 soochow univ peo...
2 篇 yanshan univ col...
2 篇 chosun univ dept...
2 篇 atilim univ dept...
2 篇 amer univ beirut...
2 篇 univ teknol mara...
1 篇 univ nigeria fac...
1 篇 fudan univ key l...
1 篇 bauman moscow st...
1 篇 univ michigan de...
1 篇 natl changhua un...
1 篇 taif univ dept m...
1 篇 st francis xavie...
1 篇 department of en...
1 篇 univ w indies de...
1 篇 qatar univ dept ...
1 篇 indian inst tech...

作者

3 篇 maier hr
3 篇 dandy gc
2 篇 chen tien-chi
2 篇 suah fbm
2 篇 jiang yi
2 篇 yang laurence t.
2 篇 zhang qingchen
2 篇 mahapatra rn
2 篇 pratihar d. k.
2 篇 kim jeong-seob
2 篇 misra sanjay
2 篇 ma pinghua
2 篇 ahmad m
2 篇 mahapatra s
2 篇 chen zhikui
2 篇 jung seul
2 篇 ren tsai-jiun
2 篇 fan hongfu
2 篇 li ke
2 篇 wang rui

语言

187 篇 英文
19 篇 其他
3 篇 中文

检索条件"主题词=Back-propagation algorithm"

共 209 条记录，以下是161-170 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：

Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks

Autoreclosure in Extra High Voltage Lines using Taguchi's Me...

引用

International Conference on Computer Engineering and Technology

作者： Zahlay, Desta F. Rao, K. S. Rama Univ Teknol PETRONAS Dept Elect & Elect Engn Tronoh 31750 Perak Malaysia

ISBN: (纸本)9780769535210

This paper presents a method to discriminate a temporary fault from a permanent one in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error back-propagation, Levenberg Marquardt algorithm and Resilient back-propagation training algorithms together with Taguchi's Method. The algorithms are developed using MATLAB software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems, and the spectra of the fault data are analyzed using fast Fourier transform to extract features of each type of fault. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.

关键词： Autoreclosure EHV transmission line faults artificial neural networks Levenberg Marquardt algorithm back-propagation algorithm RPROP Taguchi's method

来源：评论

学校读者我要写书评

暂无评论

Combined Economic and Emission Dispatch by ANN with backprop algorithm using variant learning rate & momentum coefficient

Combined Economic and Emission Dispatch by ANN with backprop...

引用

International Power Engineering Conference (IPEC)

作者： Kar, B. Mandal, K. K. Pal, D. Chakraborty, N. Univ Jadavpur Power Engn Dept Kolkata 700032 W Bengal India

ISBN: (纸本)9810557027

A Multi-layered Feed-forward Artificial Neural Network (ANN) trained by back-propagation algorithm is used to solve the problem of Combined Economic and Emission Dispatch in this paper. The system of generation associates thermal generators and emission involves. oxides of nitrogen only. Equality constraints on power balance as well as inequality constraints on generation capacity limits of the generators and transmission loss are also considered. The idea is to minimize total fuel cost of the system and control emission. The problem is first optimized by Lagrange Multiplier technique and the result is used to train the ANN wherein tuning parameters eta & alpha are altered to check their effect on convergence rate. The trained ANN is then used to generate test data. It is found that the convergence characteristic of the algorithm is excellent and the results achieved by the proposed method are quite accurate and faster in comparison to the conventional method.

关键词： Artificial Neural Network back-propagation algorithm Combined Economic and Emission Dispatch Economic Load Dispatch

来源：评论

学校读者我要写书评

暂无评论

A Design Methodology for Energy Aware Neural Networks

A Design Methodology for Energy Aware Neural Networks

引用

7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Dabbagh, Mehiar Hajj, Hazem Chehab, Ali El-Hajj, Wassim Kayssi, Ayman Mansour, Mohammad Amer Univ Beirut Dept Elect & Comp Engn Beirut Lebanon

ISBN: (纸本)9781424495375

The increasing demand for mobile devices and high performance computing has made energy consumption a main issue in computer technology. Mobile devices require extended battery life, but the available technology still puts limits on the need for recharging the devices. High performance computing has a high price tag on energy for compute-intensive applications such as data mining. As a result, optimizations at various layers of the computer platform are becoming necessary to minimize energy usage or extend the time before a battery needs to be recharged. This paper focuses on back-propagation neural network algorithm, one of the popular compute-intensive data mining algorithms. The goal is to present a design methodology for developing an energy aware algorithm. The key idea revolves around identifying operations called kernels, which are frequently used in the algorithm, and that can be implemented in hardware. Optimizing these kernels for performance or energy would then lead to a major impact in these areas. These kernels are analyzed for their impact on the overall application energy using energy-based asymptotic analysis. The methodology then considers additional optimizations not related to kernels, but are specific to the back-propagation algorithm. Suggestions are provided to improve the performance and reduce energy consumption. Experiments show that there are significant potentials in energy reduction through the use of alternative lower energy kernels or through custom optimizations with tradeoffs in the accuracy of the results.

关键词： Data Mining Neural Networks back-propagation algorithm Energy Aware

来源：评论

学校读者我要写书评

暂无评论

Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

引用

2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network back-propagation algorithm Particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Radial Basis Function Neural Network with Sliding Mode Control for Robotic Manipulators

Radial Basis Function Neural Network with Sliding Mode Contr...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Lu, Hung-Ching Tsai, Cheng-Hung Chang, Ming-Hung Tatung Univ Dept Elect Engn Taipei 104 Taiwan Univ Sci & Technol China Dept Elect Engn Taipei 104 Taiwan

ISBN: (纸本)9781424465880

In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Radial basis function uses curve fitting mode to obtain the nonlinear mapping. The unavoidable learning procedure degrades its transient performance in the existence of disturbance. Sliding-mode control is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. For this defect, a saturation function is utilized to improve it. The back-propagation (BP) algorithm and Lyapunov stability theorem are used to decide a suitable update law and sliding-mode switch gain, respectively. Thus, the satisfied performance will be obtained, which better than the controller with single RBFN controller, sliding mode controller. The simulated results of a two-link robotic manipulator for the joint frictions, changing link masses and adding external disturbances are provided to show that the effectiveness of the proposed control scheme.

关键词： radial basis function network neural network sliding-mode controller back-propagation algorithm two-link robotic manipulator

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Network Based Solution of Fractional Vibration Model 8th

Artificial Neural Network Based Solution of Fractional Vibra...

引用

8th National Conference on Wave Mechanics and Vibrations (WMVC)

作者： Mall, Susmita Chakraverty, S. Natl Inst Technol Rourkela Dept Math Rourkela 769008 Odisha India

ISBN: (纸本)9789811502873;9789811502866

The purpose of the investigation is to handle the fractional vibration problem using the multilayer artificial neural network (ANN) method. Fractional calculus has found several applications in different fields of physical systems, viz., viscoelasticity, dynamics, and anomalous diffusion transport. Fractional derivatives are practically described viscoelasticity features in structural dynamics. In general, damping models involve ordinary integer differential operators that are relatively easy to handle. On the other hand, fractional derivatives give better models with respect to the vibration systems in comparison to classical integer-order models. Here, the fractional order in the damping coefficient has been considered. We have employed the multilayer feed-forward neural architecture and error back-propagation algorithm with unsupervised learning for minimizing the error function and modification of the parameters (weights and biases). The results obtained by the present method are compared with the analytical results and are found to be in good agreement.

关键词： Fractional differential equation Vibration problem Bagley-Torvik problem Artificial neural network model Feed-forward structure back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer 4

Advanced speed sensorless control strategy for induction mac...

引用

4th International Conference on Materials and Environmental Science (ICMES)

作者： El Merrassi, Weam Abounada, Abdelouahed Ramzi, Mohamed Univ Sultan Moulay Slimane Fac Sci & Technol Dept Elect Engn Lab Automat Energy Convers & Microelect LACEM BP 23000 Beni Mellal Morocco

High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Fourth edition of the International Conference on Materials & Environmental Science.

关键词： Artificial neural network (ANN) Model reference adaptive system (MRAS) Induction machine Speed sensorless control back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Modelling of Fuzzy Logic Controller of a Maximum Power Point Tracker Based on Artificial Neural Network 16

Modelling of Fuzzy Logic Controller of a Maximum Power Point...

引用

16th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Benkercha, Rabah Moulahoum, Samir Colak, Ilhami Univ Medea Res Lab Elect Engn & Automat LREA Medea Algeria Nisantasi Univ Fac Engn & Architecture Istanbul Turkey

ISBN: (纸本)9781538614174

The Grid Connected Photovoltaic System (GCPV) has become more used system in renewable energy. Several researches have been carried out to improve the efficiency and the decrease of energy losses. One of the important components used to increase the efficiency is the DC/DC boost converter. In this paper, a new hybrid model is proposed to control the DC/DC converter, this new controller is built on the fuzzy logic controller (FLC) and artificial neural network (ANN). The pathway taken to build the model is divided into three steps, the first step is to generate a data based on the FLC, the next step is to choose an ANN structure for modeling the FLC and the last step is the test and the validation of the obtained model. The phase of building an ANN is achieved by supervised learning based on back-propagation algorithm. This algorithm is used to train the ANN model by searching of the optimal weights and thresholds that has been a minimal root mean square error between the FLC output and the ANN model. The validation test was performed with various irradiation values between the both intelligent controllers and classical P&O algorithm simultaneously.

关键词： Grid connected PV system DC/DC boost converter Maximum Power Point Tracker Fuzzy Logic Controller Artificial Neural Network back-propagation algorithm Hybrid Neurone Fuzzy

来源：评论

学校读者我要写书评

暂无评论

DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR

DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESS...

引用

International Conference on Neural Computation Theory and Applications

作者： Chen, Tien-Chi Ren, Tsai-Jiun Lou, Yi-Wei Kun Shan Univ Dept Elect Engn Tainan Taiwan Kun Shan Univ Dept Informat Engn Tainan Taiwan

ISBN: (纸本)9789898425843

The traveling-wave ultrasonic motor (TWUSM) has significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, no electromagnetic interference. The TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. This paper presents a novel control scheme, recurrent fuzzy neural network (RFNN) and general regression neural network (GRNN) controller, for a TWUSM control. The RFNN provides a real-time control such that the TWUSM output can track the reference command. The back-propagation algorithm is applied in the RFNN to automatically adjust the parameters on-line. The adaptive laws of the RFNN are derived by Lyapunov theorem such that the stability of the system can be absolute. The GRNN controller is appended to the RFNN controller to compensate the dead-zone of the TWUSM system using a predefined set. The experimental results are provided to demonstrate the effectiveness of the proposed controller.

关键词： Traveling-wave ultrasonic motor TWUSM Recurrent fuzzy neural network RFNN back-propagation algorithm Lyapunov theorem General regression neural network GRNN Dead-zone

来源：评论

学校读者我要写书评

暂无评论

Application of Artificial Neural Network to Predict Short-term Capital Flow

Application of Artificial Neural Network to Predict Short-te...

引用

International Conference on Research Challenges in Computer Science

作者： Wang, Xiping N China Elect Power Univ Dept Econ & Management Baoding 071003 Peoples R China

ISBN: (纸本)9780769539270

The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional back-propagation algorithm, the Levenberg-Marquardt algorithm was used to train the neural network. The simulation results indicate that the predictive model can be used to carry out the prediction of short-term capital flow.

关键词： artificial neural network short-term capital flow predicting Levenberg-Marquardt algorithm back-propagation 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21页 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：