检索结果-内蒙古大学图书馆

24th Chinese Control and Decision Conference (CCDC)

作者： Tolouei, H. Kouhi, A. Rasouli, H. Rasouli, C. KN Toosi Univ Technol Control Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran Plasma Phys & Nuclear Fus Inst Inst Nucl Sci & Technol AEOI Tehran Iran

ISBN: (纸本)9781457720727

In this study, nonlinear neural network controller will be developed to control plasma radial motion in Damavand Tokamak. It is essential to have a good model in order to design a proper controller for plasma radial motion. To achieve this goal, actuator circuits are simulated and in consequence based on simulator model and simulated actuator circuits nonlinear neural network controller will be designed in Damavand Tokamak. Comparison between neural network controller output and PD controller output shows the efficiency of proposed approach.

关键词： Tokamak Actuator circuits nonlinear neural network controller gradient descent algorithm Momentum algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of Multi-objective Artificial Immune Optimal algorithm in the Elevator Group Control System

Application of Multi-objective Artificial Immune Optimal Alg...

引用

3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Liu Yue-min Zhu Yan Henan Univ Sci & Technol Sch Elect Informat Engn Luoyang 471003 Peoples R China

ISBN: (纸本)9783037852866

With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal process. This paper analyzes both the advantages and the disadvantages of artificial immune algorithm and gradient descent algorithm, optimizes artificial immune algorithm, then proposes a novel optimal hybrid algorithm;at the same time, uses this hybrid algorithm in the elevator group control system combined with Pareto solution set. Making a comparison between the hybrid algorithm and the standard artificial immune algorithm, it's clear that this hybrid algorithm has certain feasibility and superiority, and to some extent, has improved the overall performance and service quality of the elevator group control system. This paper has provided a new method and a new thought on determination of the multi-objective weighted values in the elevator group control system.

关键词： Artificial Immune optimal algorithm gradient descent algorithm multi-objective optimization elevator group control system Pareto solution set

来源：评论

学校读者我要写书评

暂无评论

Fast algorithm of Evolutional Learning Neural Network

Fast Algorithm of Evolutional Learning Neural Network

引用

2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

作者： Zhong-hua Xu Wei-bing Chen Wei-feng Yang Fengnian Liu School of Computer and ommunication Hunan University of Technology Zhuzhou Hunan 412008 China

ISBN: (纸本)9781457721205

The neural networks have been widely applied to optimum calculation and solution of complicated problems. In particular, the evolutional learning neural network has better characteristic and higher precision than other neural networks. But the slow computational rate of evolutional learning and the local-optimum of the evolutional learning neural network seriously influence its application. In this paper, the fast algorithm combining the gradient descent algorithm with the evolutional learning algorithm can effectively solve above problems. This neural network has been extensively applied.

关键词： Evolutional learning Neural networks gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Localization in Wireless Sensor Networks with gradient descent

Localization in Wireless Sensor Networks with Gradient Desce...

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Qiao, Dapeng Pang, Grantham K. H. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781457702518

In this article, we present two distance-based sensor network localization algorithms. The location of the sensors is unknown initially and we can estimate the relative locations of sensors by using knowledge of inter-sensor distance measurements. Together with the knowledge of the absolute locations of three or more sensors, we can also determine the locations of all the sensors in the wireless network. The proposed algorithms make use of gradient descent to achieve excellent localization accuracy. The two gradient descent algorithms are iterative in nature and result is obtained when there is no further improvement on the accuracy. Simulation results have shown that the proposed algorithms have better performance than existing localization algorithms. A comparison of different methods is given in the paper.

关键词： Accuracy Adaptation models Distance measurement Equations Estimation Handheld computers Mathematical model distance measurement distance-based sensor network localization algorithm gradient descent algorithm gradient methods inter-sensor distance measurem

来源：评论

学校读者我要写书评

暂无评论

GMM-Guided gradient descent Learning of RBF Neural Network with Its Application on Robust GPS Satellites Selection

GMM-Guided Gradient Descent Learning of RBF Neural Network w...

引用

1st International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Saraf, M. Mohammadi, K. Mosavi, M. R. Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

ISBN: (纸本)9781467357135;9781467357128

In this paper, a novel gradient descent learning algorithm based on Gaussian Mixture Model (GMM) applied on Radial Basis Function Neural Network (RBFNN) is proposed in order to approximate the functions which have high non-linear order. How we can choose the strategy of gradient descent including learning coefficients selecting and really is it optimized to learn the same for all feature vectors?, are the challenges made us to think precisely on those. In this study, GMM estimates the probability density of the feature space and then the optimal learning rates can be evaluated proportional to these probabilities. These cause the neurons to learn correspondence with the feature distribution likelihoods. Considering robust satellite subset selection, Geometric Dilution of Precision (GDOP) factor is calculated for all subset of satellites and then the subset with lowest value is selected for positioning, but it is so non-linear and has computational burden to navigation systems. We use the proposed method to approximate it. The results on real GPS measurements demonstrate that it significantly track the non-linearity of GPS GDOP.

关键词： Gaussian Mixture Model (GMM) Geometric Dilution of Precision (GDOP) gradient descent algorithm Radial Basis Function Neural Network (RBFNN)

来源：评论

学校读者我要写书评

暂无评论

MODELING AND PREDICTIVE CONTROL USING HYBRID INTELLIGENT TECHNIQUES FOR A NONLINEAR MULTIVARIABLE PROCESS

引用

INSTRUMENTATION SCIENCE & TECHNOLOGY 2011年第2期39卷 211-230页

作者： Subathra, B. Radhakrishnan, T. K. Natl Inst Technol Dept Chem Engn Tiruchirappalli 620015 Tamil Nadu India

A recurrent neuro fuzzy network (RNFN) model-based multistep ahead predictive control strategy is proposed in this article. The fuzzy logic (FL) and neural networks (NN) are intelligent system approaches, and they complement each other. Hybridization of FL and NN utilizes the concepts of human cognitive capabilities and biological systems, respectively. Dynamic processes necessitate past information about the process input/output variables. In order to store the information, a memory unit is introduced between the fuzzy inference layer and the fuzzification layer. This recurrent structure enhances the prediction capability;hence, this RNFN model can be used to develop the multistep ahead predictive controller. The objective function of model based controller (MPC) minimizes the future control moves. The gradient descent (GD) algorithm is used to the optimize control moves. The proposed RNFN model is used to develop a model predictive controller. The performance of the RNFN-MPC is compared with that of a neuro fuzzy network (NFN)-based MPC for a laboratory scale quadruple tank process.

关键词： gradient descent algorithm hybrid soft computing intelligent techniques modeling MPC recurrent neuro fuzzy network

来源：评论

学校读者我要写书评

暂无评论

Prediction of magnetic field near power lines by normalized radial basis function network

引用

ADVANCES IN ENGINEERING SOFTWARE 2011年第11期42卷 934-938页

作者： Rankovic, Vesna Radulovic, Jasna Univ Kragujevac Fac Mech Engn Kragujevac 34000 Serbia

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This work involved the computation of the magnetic field generated by 110 kV overhead power lines using a normalized radial basis function (NRBF) network. Training of the evolving NRBF network is achieved by using the data generated from the numerical simulation based on Charge Simulation method (CSM). Then, NRBF has been used to determine the magnetic field distribution in a new geometry differing from the geometries used for training. These test results show that proposed NRBF network can be used as useful tool to calculate the magnetic fields from power lines, alternative to the conventional methods. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Prediction Magnetic fields Power lines NRBF network Training set gradient descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Supervised Learning in a Single Layer Dynamic Synapses Neural Network

Supervised Learning in a Single Layer Dynamic Synapses Neura...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yousefi, Ali Dibazar, Alireza A. Berger, Theodore W. Univ So Calif Neural Dynam Lab Los Angeles CA 90089 USA

ISBN: (纸本)9781424496365

The main focus of this paper is to introduce a new supervised learning algorithm for spiking neural networks. The learning algorithm minimizes the overall differences between spike times of target and test spike trains by utilizing a new quantitative similarity measure which has been defined in this work. The actual membrane potential of a post-synaptic neuron is adjusted at the time of spikes based on what has been measured from similarity measure in order to generate the desired membrane potential. Finally, by utilizing gradient descent algorithm, the parameters of the spiking neural network are tuned to generate the desired output membrane potential. The proposed algorithm was applied to tune the facilitation, depression, and synaptic weight constants of the Dynamic Synapses Neural Network - DSNN - for the aim of input-output functional mapping. The simulation results show that the system identification task converges to the global optimum. The rate-to-time coding simulation performs with more than 75 percent accuracy. The performance of both system identification and rate-to-time coding is due to adaptation of short and long term synaptic parameters which cannot be accomplished if only synaptic weight is adapted.

关键词： Adaptation models Calcium Energy states Filtering Heuristic algorithms Mathematical model Neurons depression weight constant facilitation weight constant gradient descent algorithm gradient methods input-output functional mapping learning (artificial intelligence) neural nets post-synaptic neuron rate-to-time coding simulation single layer dynamic synapses neural network spike train supervised learning algorithm synaptic weight constant

来源：评论

学校读者我要写书评

暂无评论

Learning to Rank Relational Objects Based on the Listwise Approach

Learning to Rank Relational Objects Based on the Listwise Ap...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ding, Yuxin Zhou, Di Xiao, Min Dong, Li Harin Inst Technol Shenzhen Grad Sch Dept Comp Sci & Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781424496365

In recent years machine learning technologies have been applied to ranking, and a new research branch named "learning to rank" has emerged. Three types of learning-to-rank methods - pointwise, pairwise and listwise approaches - have been proposed. This paper is concerned with listwise approach. Currently structural support vector machine(SVM) and linear neural network have been utilized in listwise approach, but these methods only consider the content relevance of an object with respect to queries, they all ignore the relationships between objects. In this paper we study how to use relationships between objects to improve the performance of a ranking model. A novel ranking function is proposed, which combines the content relevance of documents with respect to queries and relation information between documents. Two types of loss functions are constructed as the targets for optimization. Then we utilize neural network and gradient descent algorithm as model and training algorithm to build ranking model. In the experiments, we compare the proposed methods with two conventional listwise approaches. Experimental results on OHSUMED dataset show that the proposed methods outperform the conventional methods.

关键词： Entropy Feature extraction Machine learning Stochastic processes Training Vectors Web pages content relevance gradient descent algorithm gradient methods learning (artificial intelligence) learning-to-rank method linear neural network listwise approach loss functions machine learning neural nets optimisation optimization pairwise approach pointwise approach ranking function ranking model relational objects structural support vector machine support vector machines

来源：评论

学校读者我要写书评

暂无评论

UNMIXING HYPERSPECTRAL IMAGES USING THE GENERALIZED BILINEAR MODEL

UNMIXING HYPERSPECTRAL IMAGES USING THE GENERALIZED BILINEAR...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Halimi, Abderrahim Altmann, Yoann Dobigeon, Nicolas Tourneret, Jean-Yves Univ Toulouse IRIT INP ENSEEIHT TeSA Toulouse France

ISBN: (纸本)9781457710056

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

关键词： hyperspectral imagery spectral unmixing bilinear model Bayesian inference MCMC methods gradient descent algorithm least square 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：