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

1,679 篇 会议
213 篇 期刊文献

馆藏范围

1,892 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

315 篇 工学
- 159 篇 计算机科学与技术...
- 157 篇 电气工程
- 62 篇 控制科学与工程
- 46 篇 电子科学与技术（可...
- 30 篇 软件工程
- 21 篇 信息与通信工程
- 14 篇 机械工程
- 13 篇 仪器科学与技术
- 6 篇 测绘科学与技术
- 6 篇 生物医学工程（可授...
- 4 篇 材料科学与工程（可...
- 4 篇 动力工程及工程热...
- 3 篇 核科学与技术
- 3 篇 网络空间安全
- 2 篇 力学（可授工学、理...
- 2 篇 石油与天然气工程
- 2 篇 船舶与海洋工程
- 2 篇 航空宇航科学与技...
- 2 篇 生物工程
49 篇 理学
- 15 篇 物理学
- 12 篇 系统科学
- 9 篇 数学
- 6 篇 地球物理学
- 3 篇 生物学
- 2 篇 化学
39 篇 管理学
- 37 篇 管理科学与工程(可...
14 篇 医学
- 10 篇 临床医学
- 4 篇 基础医学(可授医学...
- 2 篇 特种医学
4 篇 农学
- 3 篇 作物学
2 篇 军事学
1 篇 文学
1 篇 艺术学

主题

1,892 篇 backpropagation ...
1,270 篇 neural networks
463 篇 artificial neura...
453 篇 neurons
327 篇 multi-layer neur...
297 篇 convergence
289 篇 feedforward neur...
211 篇 multilayer perce...
197 篇 computer network...
191 篇 testing
126 篇 algorithm design...
125 篇 pattern recognit...
122 篇 computer science
119 篇 intelligent netw...
112 篇 recurrent neural...
110 篇 equations
107 篇 fuzzy neural net...
106 篇 training data
100 篇 control systems
100 篇 signal processin...

机构

12 篇 school of electr...
11 篇 department of el...
9 篇 department of el...
7 篇 department of el...
5 篇 at and t bell la...
5 篇 computer science...
5 篇 faculty of engin...
4 篇 school of inform...
4 篇 department of el...
4 篇 department of el...
4 篇 center for infor...
4 篇 departamento de ...
4 篇 center for robot...
4 篇 department of co...
4 篇 departamento de ...
4 篇 department of el...
4 篇 esstt tunis tuni...
3 篇 international co...
3 篇 department of co...
3 篇 department of el...

作者

13 篇 wen yu
9 篇 n.j. bershad
8 篇 k. nakayama
8 篇 k.y. lee
7 篇 h.k. kwan
7 篇 j.c. principe
7 篇 j.j. shynk
6 篇 w.c. miller
6 篇 n.k. loh
6 篇 m.t. hagan
6 篇 p. saratchandran
6 篇 chin-teng lin
6 篇 xiaoou li
6 篇 k. hirasawa
6 篇 n. sundararajan
5 篇 g.v. puskorius
5 篇 l.a. feldkamp
5 篇 f. fnaiech
5 篇 f. piazza
5 篇 d.w. tufts

语言

1,789 篇 英文
96 篇 其他
7 篇 中文

检索条件"主题词=Backpropagation Algorithms"

共 1892 条记录，以下是461-470 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：

An Ensemble Learning algorithm for Blind Signal Separation Problem

An Ensemble Learning algorithm for Blind Signal Separation P...

引用

International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Yan Li Peng Wen Department of Mathematics and Computing University of Southern Queensland QLD Australia University of Southern Queensland QLD Australia

The framework in Bayesian learning algorithms is based on the assumptions that the quantities of interest are governed by probability distributions, and that optimal decisions can be made by reasoning about these probabilities together with the data. In this paper, a Bayesian ensemble learning approach based on enhanced least square backpropagation (LSB) neural network training algorithm is proposed for blind signal separation problem. The method uses a three layer neural network with an enhanced LSB training algorithm to model the unknown blind mixing system. Ensemble learning is applied to estimate the parametric approximation of the posterior probability density function (pdf). The Kullback-Leibler information divergence is used as the cost function in the paper. The experimental results on both artificial data and real recordings demonstrate that the proposed algorithm can separate blind signals very well

关键词： Blind source separation backpropagation algorithms Bayesian methods Probability distribution Least squares methods Neural networks Artificial neural networks Least squares approximation Probability density function Cost function

来源：评论

学校读者我要写书评

暂无评论

Anomaly intrusion detection for system call using the soundex algorithm and neural networks

Anomaly intrusion detection for system call using the sounde...

引用

IEEE Symposium on Computers and Communications (ISCC)

作者： ByungRae Cha B. Vaidya Seungjo Han Department of Computer Engineering Honam University South Korea Department of Electronics & Computer Engineering Tribhuvan University Nepal Department of Information & Communication Engineering Chosun University South Korea

To improve the anomaly intrusion detection system using system calls, this study focuses on supervisor learning neural networks using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the soundex algorithm, this study conducted neural learning by using a backpropagation algorithm. The proposed method and N-gram technique are applied for anomaly intrusion detection of system call using sendmail data of UNM to demonstrate its performance.

关键词： Intrusion detection Neural networks Computer networks backpropagation algorithms Frequency Change detection algorithms Databases Machine learning Data mining Automata

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid learning scheme for pattern recognition

A novel hybrid learning scheme for pattern recognition

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wong Lai Ping A.T.L. Phuan Nanyang Technological University Nanyang Technological University Singapore Singapore SG

This paper presents a novel hybrid learning scheme applicable to pattern classification tasks. The proposed self-supervised learning method is a combination of unsupervised clustering k-means fast learning artificial neural network (KFLANN) and a typical supervised backpropagation learning algorithm (BP Network). Complementary benefits of both algorithms motivated the development of the hybrid model. The KFLANN clustering output is used as the target value for BP training, resulting in an efficient self-supervised learning model. Experimental results are comparable with non-hybrid individual algorithm.

关键词： Pattern recognition Clustering algorithms Supervised learning Artificial neural networks backpropagation algorithms Unsupervised learning Paper technology Pulp manufacturing Pattern classification Hybrid power systems

来源：评论

学校读者我要写书评

暂无评论

Application of An Improved Particle Swarm Optimization Algorithm for Neural Network Training*

Application of An Improved Particle Swarm Optimization Algor...

引用

International Conference on Neural Networks and Brain (ICNNB)

作者： Fuqing Zhao Zongyi Ren Dongmei Yu Yahong Yang School of Computer and Communication Lanzhou University of Technology Lanzhou P.R. China College of Civil Engineering Lanzhou University of Technology Lanzhou Gansu P.R. China

Particle swarm optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart in 1995 and has been applied successfully to various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. backpropagation (BP) is generally used for neural network training. It is very important to choose a proper algorithm for training a neural network. In this paper, we present a modified particle swarm optimization based training algorithm for neural network. The proposed method modify the trajectories (positions and velocities) of the particle based on the best positions visited earlier by themselves and other particles, and also incorporates population diversity method to avoid premature convergence. Experimental results have demonstrated that the modified PSO is a useful tool for training neural network

关键词： Particle swarm optimization Neural networks backpropagation algorithms Evolutionary computation Marine animals Educational institutions Birds Humans Diversity methods Convergence

来源：评论

学校读者我要写书评

暂无评论

Hybrid optimization algorithm for the definition of MLP neural network architectures and weights

Hybrid optimization algorithm for the definition of MLP neur...

引用

International Conference on Hybrid Intelligent Systems (HIS)

作者： A.P.S. Lins T.B. Ludermir Universidade Federal de Pernambuco Recife Brazil

This paper proposes improvements in the Yamazaki method for the simultaneous optimization of multilayer perceptron network weights and architectures. The main objective is to propose a set of modifications, with respective validations, aimed at creating a more efficient optimization process. The optimization hybrid algorithm is based on the simulated annealing and tabu search algorithms as well as the Yamazaki method. The modifications are carried out in the implementation criteria, such as the neighbor generation mechanism and cooling schedule. One of the main points of this paper is the creation of a new neighbor generation mechanism aimed at increasing the search space. Experimental results, using two different data sets (wine and gases), show that the optimization hybrid algorithm obtained the lowest value of the average classification error and obtained a substantial gain in execution time (reduced by an average of 46.72%).

关键词： Neural networks Simulated annealing Databases Optimization methods Gases Multilayer perceptrons Cooling backpropagation algorithms Nose Multi-layer neural network

来源：评论

学校读者我要写书评

暂无评论

Prediction of Surface Roughness in Cylindrical Traverse Grinding Based on ALS Algorithm

Prediction of Surface Roughness in Cylindrical Traverse Grin...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jia-Zhong Wang Long-Shan Wang Guo-Fa Li Gui-Hong Zhou College of Mechanical Science and Engineering Jilin University Changchun China College of Mechanical & Electric engineering Agricultural University of Hebei Baoding China

A framework for modeling traverse surface roughness using fuzzy basis function neural networks (FBFN) is presented with adaptive least-squares (ALS) training algorithm. The ALS algorithm, based on the least-squares method and genetic algorithm (GA), is proposed for autonomous learning and construction of FBFN without any human intervention. Simulation and experiment studies are performed to demonstrate advantages of the proposed modeling framework with the training algorithm in modeling grinding processes. Simulation studies indicated that the new algorithms generate superior results over conventional algorithms such as backpropagation algorithms and conventional GA-based algorithm. The study on traverse grinding process using a small amount of experimental data demonstrated the potential of the ALS algorithm. The accuracy of developed models is validated through independent sets of grinding experiments.

关键词： Rough surfaces Surface roughness backpropagation algorithms Clustering algorithms Wheels Neural networks Genetic algorithms Educational institutions Agricultural engineering Fuzzy neural networks

来源：评论

学校读者我要写书评

暂无评论

The implementation of the self-calibration, self-compensation and self-validation algorithms for foundation fieldbus sensors are presented using standard function blocks

The implementation of the self-calibration, self-compensatio...

引用

IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA)

作者： E. Cagni D.R. Pereira A. Duarte Doria Neto J.D. de Melo L. Affonso H. Guedes de Oliveira Departamento de Engenharia de Computação e Automação Universidade Federal do Rio Grande do Norte Natal Natal RN Brazil Departamento de Engenharia de Computação e Automação Universidade Federal do Rio Grande do Norte Natal RN Brazil

来源：评论

学校读者我要写书评

暂无评论

Evolution strategies on connection weights into modified gradient function for multi-layer neural networks

Evolution strategies on connection weights into modified gra...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： S.C. Ng S.H. Leung A. Luk School of Science and Technology Open University HK Hong Kong China Department of Electronic Engineering City University of Hong Kong Hong Kong China St B&P Neural Investments Private Limited Westleigh NSW Australia

In this paper, two modifications on the conventional back-propagation algorithm for feedforward multi-layer neural networks are presented. One modification is based on the calculation of the gradient function, while the other one is the use of evolution strategies on connection weights into the gradient search algorithm. From simulation results, the new modified algorithm always converges to the global optimal solution with better performance when compared with other fast learning algorithms and global search methods.

关键词： Neural networks Multi-layer neural network Convergence Neurons backpropagation algorithms Investments Australia Search methods Signal processing Acceleration

来源：评论

学校读者我要写书评

暂无评论

On-Line Clustering for Nonlinear System Identification Using Fuzzy Neural Networks

On-Line Clustering for Nonlinear System Identification Using...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Wen Yu A. Ferreyra Departamento de Control Automático CINVESTAV Mexicali Mexico Departamento de Electrónica Sección de Instrumentación Universidad Autonoma Metropolitana Azcapotzalco Mexicali Mexico

In this paper we propose a novel on-line clustering approach which can be applied for nonlinear system identification. Both structure and parameters of fuzzy neural networks are updated on-line. The new clustering method for the structure identification can divide input/output data into different groups (rule number) by on-line data. For the parameter learning, our algorithm has two advantages over the others. First, the normal methods for parameter identification are based on a fixed structure and whole data, for example ANFIS by C. F. Jang and C. Teng Lin (1998), but after clustering we know each group corresponds to one rule, so we train each rule by its group data, it is more effective. Second, we give a time-varying learning rate for the common used backpropagation algorithm, we prove that the new algorithm is stable and faster than backpropagation algorithm

关键词： Nonlinear systems Fuzzy neural networks Clustering algorithms backpropagation algorithms Partitioning algorithms Neural networks Clustering methods Parameter estimation Fuzzy logic Least squares methods

来源：评论

学校读者我要写书评

暂无评论

An Accelerating Learning Algorithm for Block-Diagonal Recurrent Neural Networks

An Accelerating Learning Algorithm for Block-Diagonal Recurr...

引用

International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： P. Mastorocostas D. Varsamis C. Mastorocostas I. Rekanos Department of Informatics Technological Educational Institute of Serres Serres Greece

An efficient training method for block-diagonal recurrent neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static models, in order to be applied to dynamic systems. A comparative analysis with a series of algorithms and recurrent models is given, indicating the effectiveness of the proposed learning approach

关键词： Acceleration Recurrent neural networks Neurons Control systems backpropagation algorithms Neurofeedback Informatics Educational technology Algorithm design and analysis System identification

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190页 << < 43 44 45 46 47 48 49 50 51 52 > >>

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：