检索结果-内蒙古大学图书馆

An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch's problem

引用

NEURAL COMPUTING & APPLICATIONS 2017年第1期28卷 171-178页

作者： Yadav, Neha Yadav, Anupam Kumar, Manoj Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Natl Inst Technol Dept Sci & Humanities Garhwal 246174 Uttarakhand India Motilal Nehru Natl Inst Technol Dept Math Allahabad 211004 Uttar Pradesh India

In this article, a simple and efficient approach for the approximate solution of a nonlinear differential equation known as Troesch's problem is proposed. In this article, a mathematical model of the Troesch's problem is described which arises in confinement of plasma column by radiation pressure. An artificial neural network (ANN) technique with gradient descent and particle swarm optimization is used to obtain the numerical solution of the Troesch's problem. This method overcomes the difficulty arising in the solution of Troesch's problem in the literature for eigenvalues of higher magnitude. The results obtained by the ANN method have been compared with the analytical solutions as well as with some other existing numerical techniques. It is observed that our results are more approximate and solution is provided on continuous finite time interval unlike the other numerical techniques. The main advantage of the proposed approach is that once the network is trained, it allows evaluating the solution at any required number of points for higher magnitude of eigenvalues with less computing time and memory.

关键词： Artificial neural network technique backpropagation algorithm Plasma column Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Twisted Quaternary Neural Networks

引用

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第4期7卷 397-401页

作者： Kobayashi, Masaki Nakajima, Akihiro Univ Yamanashi Interdisciplinary Grad Sch Med & Engn Kofu Yamanashi 4008511 Japan

The quaternary neural network (QNN) proposed by Nitta is a high-dimensional neural network. Nitta showed that its learning is faster than that of ordinary neural networks and the number of required parameters is almost one-third of that of real-valued neural networks by computer simulations. In this paper, we propose the twisted quaternary neural network (TQNN) which modifies the directions of multiplications of the QNN. Since quaternions are noncommutative on multiplication, we can get another neural network. When the activation function is linear, multilayered neural networks can be expressed by single-layered neural networks. But the TQNN cannot be expressed by a single-layered QNN even if the activation function is linear. Therefore, the TQNN is expected to produce a variety of signal-processing systems. We performed computer simulations to compare the QNN and the TQNN. Then we found that the latter's learning is a little faster. Moreover, computer simulation showed that the QNN tended to be trapped in local minima or plateaus but the TQNN did not. It is said that reducibility causes local minima and plateaus. We discuss the differences of reducibility between the QNN and the TQNN as well. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： neural networks quaternion learning backpropagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Compact building blocks for artificial neural networks

引用

ELECTRONICS LETTERS 1999年第1期35卷 56-57页

作者： Meléndez-Rodríguez, M Silva-Martínez, J Natl Inst Astrophys Opt & Elect Integrated Circuit Design Grp Puebla 72000 Mexico

A four quadrant analogue multiplier, an activation function and a differentiator intended for the implementation of backpropagation algorithms are proposed. The priorities for the design are low-power, low-voltage, an... 详细信息

关键词： differentiating circuits neural nets artificial neural network Neural net devices Neural nets (circuit implementations) analogue multipliers silicon area activation function analogue multiplier backpropagation differentiator backpropagation algorithm low-power electronics low-power low-voltage circuit building block

来源：评论

学校读者我要写书评

暂无评论

Experimental Investigation on the Influence of Graphene Nanoplatelets Dispersion on the Thermal Conductivity of Sunflower Oil

引用

INTERNATIONAL JOURNAL OF NANOSCIENCE 2020年第2期19卷

作者： Muthuraj, M. Raj, J. Bensam Sunil, J. Vidyaa Vikas Coll Engn & Technol Dept Mech Engn Namakal 637214 Tamil Nadu India Nadar Saraswathi Coll Engn & Technol Dept Mech Engn Theni 625531 Tamil Nadu India VV Coll Engn Dept Mech Engn Tirunelveli 628656 Tamil Nadu India

In the past decades, considerable efforts have been made for the development of energy-efficient and eco-friendly convective heat transfer and lubricating agents because of growing energy demands, precision manufacturing, miniaturization and sustainability issues. In this study, different concentrations of graphene-sunflower oil nanofluid were prepared and their thermal conductivity was experimentally investigated and compared with the correlations of similar researches found in the literature. The morphology of graphene nanoplatelets was appraised by X-ray diffractometer (XRD) and scanning electron microscope (SEM). The results show that the thermal conductivity of nanofluid was enhanced with temperature and nanoparticles weight fraction. The nanoconvection at high temperatures, less meandering mobility of graphene nanoplatelets and high kinematic viscosity of graphene nanofluids at low temperatures were identified as the key factors for the thermal conductivity enhancement. Further, the concentration and temperature-dependent theoretical correlation were proposed for estimating the thermal conductivity of graphene nanofluids using backpropagation algorithm of artificial neural network (ANN) with the minimum margin of deviation.

关键词： Eco-friendly thermal conductivity graphene nanoplatelets kinematic viscosity backpropagation algorithm artificial neural network

来源：评论

学校读者我要写书评

暂无评论

Stacking-based neural network for nonlinear time series analysis

引用

STATISTICAL METHODS AND APPLICATIONS 2024年第3期33卷 901-924页

作者： De Alwis, Tharindu P. Samadi, S. Yaser Worcester Polytech Inst Dept Math Sci Worcester MA USA Southern Illinois Univ Carbondale Sch Math & Stat Sci Carbondale IL 62901 USA

Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model. This paper introduces a stacked generalization scheme specifically designed for nonlinear time series models. Instead of selecting a single model using traditional model selection criteria, our approach stacks several nonlinear time series models from different classes and proposes a new generalization algorithm that minimizes prediction error. To achieve this, we utilize a feed-forward artificial neural network (FANN) model to generalize existing nonlinear time series models by stacking them. Network parameters are estimated using a backpropagation algorithm. We validate the proposed method using simulated examples and a real data application. The results demonstrate that our proposed stacked FANN model achieves a lower error and improves forecast accuracy compared to previous nonlinear time series models, resulting in a better fit to the original time series data.

关键词： Stacked generalization Cross-validation Time series Feed-forward artificial neural network (FANN) backpropagation algorithm C4 Econometric and Statistical Methods: Special Topics C45 Neural Networks and Related Topics

来源：评论

学校读者我要写书评

暂无评论

Chaos and neural network learning. Some observations

引用

NEURAL PROCESSING LETTERS 1998年第2期7卷 69-80页

作者： Bertels, K Neuberg, L Vassiliadis, S Pechanek, DG Univ Namur Dept Business Adm B-5000 Namur Belgium Tech Univ Delft Dept Elect Engn NL-2628 CD Delft Netherlands IBM Corp Microelect Div Res Triangle Pk NC 27709 USA

In this paper, we investigate the impact of chaos on the learning process of the XOR-boolean function by backpropagation neural networks. It has been shown previously that such networks exhibit chaotic behavior but it has never been studied whether chaos enhances or prohibits learning. We show that chaos (when learning the XOR-boolean function) does indeed allow learning but our findings do not indicate any positive role of chaos for learning. In particular, we found that the temperature parameter in the backpropagation algorithm causes the parameter regime, as represented by means of a bifurcation diagram, to shift to the right. We furthermore found that as less chaos appears during the learning process, the faster, on the average, a neural network learned the XOR-function.

关键词： backpropagation algorithm bifurcation chaos neural network learning parametrization

来源：评论

学校读者我要写书评

暂无评论

Diagnosing failed distribution transformers using neural networks

引用

IEEE TRANSACTIONS ON POWER DELIVERY 2001年第4期16卷 631-636页

作者： Farag, AS Mohandes, M Al-Shaikh, A King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia SCECO E EDSD EED Dammam Saudi Arabia

An Artificial Neural Networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing Back Propagation algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transform, the weather condition, if there are any damaged bushings?, if there are any damaged casing or enclosure?, if there is oil leakage?, and if there are any faults in the windings?. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained. with the proposed approach applied and with the analysis of the attainable results.

关键词： backpropagation bushings distribution networks failure analysis fault diagnosis neural nets power engineering computing power transformer insulation power transformers transformer oil transformer windings ANN training backpropagation algorithm damaged bu ELECTRIC TRANSFORMERS Transformer windings backpropagation algorithms transformer oil transformer insulation back propagation Power engineering computing Fault diagnosis Power grids Neural network FAILURE ANALYSIS

来源：评论

学校读者我要写书评

暂无评论

Design and development of logistics workflow systems for demand management with RFID

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5428-5437页

作者： Lee, C. K. M. Ho, William Ho, G. T. S. Lau, H. C. W. Nanyang Technol Univ Div Syst & Engn Management Sch Mech & Aerosp Engn Singapore 639798 Singapore Aston Univ Operat & Informat Management Grp Aston Business Sch Birmingham B4 7ET W Midlands England Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Western Sydney Sch Management Penrith NSW 2751 Australia

This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today's globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Logistics workflow RFID Artificial neural network backpropagation algorithm Demand chain management Supply chain management

来源：评论

学校读者我要写书评

暂无评论

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 2002年第2期13卷 393-401页

作者： Sinha, SK Karray, F Penn State Univ Civil & Environm Engn University Pk PA 16801 USA Univ Waterloo Syst Design Engn Waterloo ON NSL 3GL Canada

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

关键词： backpropagation algorithm crack features image processing neural networks neuro-fuzzy algorithms pipe defect classification pipeline inspection

来源：评论

学校读者我要写书评

暂无评论

NN-AirPol: a neural-networks-based method for air pollution evaluation and control

引用

INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION 2006年第3-4期28卷 310-325页

作者： Karaca, Ferhat Nikov, Alexander Alagha, Omar Fatih Univ Dept Environm Engn TR-34900 Istanbul Turkey Fatih Univ Dept Ind Engn TR-34900 Istanbul Turkey

A method for air pollution evaluation and control, based on one of the most popular neural networks - the backpropagation algorithm, is proposed. After the backpropagation training, the neural network, based on weather forecasting data, determines the future concentration of critical air pollution indicators. Depending on these concentrations, relevant episode warnings and actions are activated. A case study is carried out to illustrate and validate the method proposed, based on Istanbul air pollution data. Sulphur dioxide and inhalable particulate matter are selected as air pollution indicators (neural network outputs). Relevant episode measures are proposed. Among ten backpropagation algorithms, the BFGS algorithm (Quasi-Newton algorithms) is adopted since it showed the lowest training error. A comparison of NN-AirPol method against regression and perceptron models showed significantly better performance.

关键词： air pollution modelling backpropagation algorithm optimisation

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：