检索结果-内蒙古大学图书馆

Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2000年第3期98卷 299-309页

作者： Dutta, RK Paul, S Chattopadhyay, AB Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

The search for an adequate strategy for on-line Tool Condition Monitoring (TCM) in an automated manufacturing environment is a distant goal, yet to be achieved. The present impetus is towards the application of neural networks with different learning schemes through the use of computers for faster processing. In this paper, the performance of the back-propagation neural network has been studied for various parameters. Moreover, the efficacy of a modified back-propagation algorithm for faster convergence has been evaluated for its applicability with a set of data on TCM, where reduction in computation time is very important. The results of the modified algorithm are quite encouraging for future applications in the area of on-line TCM. (C) 2000 Published by Elsevier Science S.A. All rights reserved.

关键词： tool condition monitoring neural network back-propagation algorithm modified back-propagation algorithm for faster convergence

来源：评论

学校读者我要写书评

暂无评论

Implementation of the RBF neural chip with the back-propagation algorithm for on-line learning

引用

APPLIED SOFT COMPUTING 2015年 29卷 233-244页

作者： Kim, J. S. Jung, S. Samsung Elect Suwon South Korea Chungnam Natl Univ Dept Mechatron Engn ISEE Taejon South Korea

This article presents the hardware implementation of the floating-point processor (FPP) to develop the radial basis function (RBF) neural network for the general purpose of pattern recognition and nonlinear control. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel calculation of the back-propagation algorithm. Internal weights of the RBF network are updated by the online learning back-propagation algorithm. The on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning process written in the MATLAB program. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the XOR logic. Performances are evaluated by comparing results from the MATLAB through extensive experimental studies. (C) 2014 Elsevier By. All rights reserved.

关键词： Floating-point processor RBF neural network back-propagation algorithm FPGA Nonlinear classifier

来源：评论

学校读者我要写书评

暂无评论

A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training

引用

NEURAL COMPUTING & APPLICATIONS 2018年第12期30卷 3707-3720页

作者： Amirsadri, Shima Mousavirad, Seyed Jalaleddin Ebrahimpour-Komleh, Hossein Kashan Univ Dept Comp & Elect Engn 6 Km Ghotbravandi Blvd Kashan Iran

In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets.

关键词： Grey wolf optimizer Multilayer perceptron Neural network training Learning Levy flight back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of bond strength model for FRP plates using back-propagation algorithm

引用

JOURNAL OF APPLIED POLYMER SCIENCE 2006年第6期100卷 5119-5127页

作者： Park, DK Jang, H Ahn, N Wonkwang Univ Div Architecture Chonbuk 570749 South Korea Wonkwang Univ Div Civil Engn Chonbuk 570749 South Korea Sejong Univ Dept Architectural Engn Seoul 143747 South Korea

For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion-proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally adhering to the plate, many researches regarding the bond strength improvement have been substantially performed. To search out such bond strength improvement, previous researchers had ever examined the bond strength of FRP plate through their experiment by setting up many variables. However, since the experiment for a research on the bond strength takes much of expenditure for setting up the equipment and is time-consuming, also is difficult to be carried out, it is limitedly conducted. The purpose of this study was to develop the most suitable artificial neural network model by application of various neural network models and algorithm to the data of the bond strength experiment conducted by previous researchers. Many variables were used as input layers against bond strength: depth, width, modulus of elasticity, tensile strength of FRP plate and the compressive strength, tensile strength, and width of concrete. The developed artificial neural network model has been applied back-propagation, and its error was learned to be converged within the range of 0.001. Besides, the process for the over-fitting problem has been dissolved by Bayesian technique. The verification on the developed model was executed by comparison with the test results of bond strength made by other previous researchers, which was never been utilized to the learning as yet. (c) 2006 Wiley Periodicals, Inc.

关键词： FRP bond strength back-propagation algorithm neural network

来源：评论

学校读者我要写书评

暂无评论

Convergence analysis of a back-propagation algorithm with adaptive momentum

引用

NEUROCOMPUTING 2011年第5期74卷 749-752页

作者： Shao, Hongmei Zheng, Gaofeng China Univ Petr Sch Math & Computat Sci Dongying 257061 Peoples R China JANA Solut Inc Minato Ku Tokyo 1050014 Japan

In this paper, the convergence of a new back-propagation algorithm with adaptive momentum is analyzed when it is used for training feedforward neural networks with a hidden layer. A convergence theorem is presented and sufficient conditions are offered to guarantee both weak and strong convergence result. Compared with existing results, our convergence result is of deterministic nature and we do not require the error function to be quadratic or uniformly convex. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Neural network back-propagation algorithm Adaptive momentum Convergence

来源：评论

学校读者我要写书评

暂无评论

A Model with back-propagation algorithm for Recovery Efficiency Forecast of Carbon Dioxide Flooding

A Model with Back-propagation Algorithm for Recovery Efficie...

引用

International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2012)

作者： Ma, Pinghua Fan, Hongfu Li, Ke China Univ Geosci Beijing Sch Energy Resources Beijing 100083 Peoples R China YanShan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9783037856499

As one of the most important displacements in producing petroleum, CO2 flooding has been developed for nearly 50 years around the world in order to improve the tertiary recovery. The recovery efficiency, R, is a key parameter in the CO2 displacement of crude oil. Traditionally, R is determined by conducting CO2 flooding experiment, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of R with mathematical models is developed in recent years, which also needs plenty of relevant parameters considered. The study reported in this paper uses artificial neural network to determine R. Five dimensionless variables are considered to analyse the CO2 immiscible displacement process. An optimal model is chosen with its suitable hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with the numerical simulation model, demonstrating the superior performance of the proposed R prediction model.

关键词： Artificial neural network Recovery Efficiency Forecast back-propagation algorithm model Carbon Dioxide Flooding

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Embedded RBF Neural Chip with back-propagation algorithm for Pattern Recognition Tasks

Evaluation of Embedded RBF Neural Chip with Back-propagation...

引用

6th IEEE International Conference on Industrial Informatics

作者： Kim, Jeong-Seob Jung, Seul Chungnam Natl Univ Intelligent Syst & Emot Engn Lab Mechatron Grp BK21 Taejon South Korea

ISBN: (纸本)9781424421701

This article presents the evaluation analysis of the radial function neural network embedded on an FPGA chip by experiments. The back-propagation algorithm has been embedded and tested for the feasibility for on-line learning tasks. The nonlinear pattern classification task of the XOR logic has been conducted by the designed hardware. Performances are evaluated extensively by different orders of the Taylor-Maclaurin series expansion for approximating nonlinear functions and compared with results by the MATLAB program. The effects on the performance by the nonlinear function approximation have been analyzed by experimental studies of the XOR classification task.

关键词： RBF neural network back-propagation algorithm floating point processor FPGA

来源：评论

学校读者我要写书评

暂无评论

A Model with back-propagation algorithm for the Estimation of Irreducible Water Saturation

A Model with Back-propagation Algorithm for the Estimation o...

引用

International Conference on Energy, Environment and Sustainable Development (ICEESD 2011)

作者： Ma, Pinghua Fan, Hongfu Li, Ke China Univ Geosci Sch Energy Resources Beijing 100083 Peoples R China YanShan Univ Coll Informat Sci & Engn Qinhuangdao Peoples R China

ISBN: (纸本)9783037852682

As one of the most important reservoir parameters, irreducible water saturation, S-wir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of S-wir. Traditionally, S-wir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of S-wir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine S-wir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed S-wir prediction models.

关键词： Artificial neural network Irreducible water saturation back-propagation algorithm model

来源：评论

学校读者我要写书评

暂无评论

An Improved back-propagation algorithm for Fuzzy Modeling

An Improved Back-propagation Algorithm for Fuzzy Modeling

引用

3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Miao, Zhinong Zheng, Huijun Panzhihua Univ Sch Informat & Elect Engn Panzhihua 617000 Sichuan Peoples R China

ISBN: (纸本)9783037850190

Fuzzy modeling is discussed in many literatures and there are numerous algorithms are proposed. back-propagation algorithm is an efficient algorithm for fuzzy modeling and many papers proposed the usage of such method. But there exists potential risk of dead zone, abrupt inference surface and decreasing sensitivity for normal back-propagation algorithm in fuzzy modeling. This paper analysis the potential problems of normal algorithm and suggest a reformative back-propagation algorithm for fuzzy modeling. A complete algorithm is presented in the paper and some simulate result is discussed

关键词： fuzzy modeling back-propagation algorithm improved algorithm

来源：评论

学校读者我要写书评

暂无评论

A Handel-C implementation of the back-propagation algorithm on field programmable gate arrays

A Handel-C implementation of the back-propagation algorithm ...

引用

International Conference on Reconfigurable Computing and FPGAs

作者： Pandya, V Areibi, S Moussa, M Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

ISBN: (纸本)0769524567

General Purpose Processors (GPPs) and ASICs have traditionally been the common means for building and implementing Artificial Neural Network's (ANNs). However Such computing paradigms suffer from the constant need of establishing a trade-off between flexibility and performance Due to the technological advance in the development of progammable logic devices, Field Programmable Gate Arrays (FPGAs) have become attractive for realizing ANNs. FPGAs have shown to exhibit excellent flexibility in terms of reprogramming the same hardware and at the same time achieving good performance by enabling parallel computation. In this paper various implementations of ANNs on FPGAs are investigated and compared. The research described in this paper proposes three partially parallel architectures and a fully parallel architecture to realize the back- propagation algorithm on an FPGA. The proposed designs are coded in Handel-C and functionally, verified by synthesizing them on a Virtex2000e FPGA chip. The partially parallel architectures and the fully parallel architecture are found to be 2.25 and 4 times faster than the software implementation rcspectively for different benchmarks.

关键词： backpropagation field programmable gate arrays neural nets parallel architectures FPGA chip Handel-C Virtex2000e artificial neural networks back-propagation algorithm Field programmable gate arrays Parallel architectures back propagation Neural network

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：