检索结果-内蒙古大学图书馆

A nonmonotone scaled conjugate gradient algorithm for large-scale unconstrained optimization

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2018年第11期95卷 2212-2228页

作者： Ou, Yigui Zhou, Xin Hainan Univ Dept Appl Math Haikou 570228 Hainan Peoples R China

This paper proposes a nonmonotone scaled conjugate gradient algorithm for solving large-scale unconstrained optimization problems, which combines the idea of scaled memoryless Broyden-Fletcher-Goldfarb-Shanno preconditioned conjugate gradient method with the nonmonotone technique. An attractive property of the proposed method is that the search direction always provides sufficient descent step at each iteration. This property is independent of the line search used. Under appropriate assumptions, the method is proven to possess global convergence for nonconvex smooth functions, and R-linear convergence for strongly convex functions. Preliminary numerical results and related comparisons show the efficiency of the proposed method in practical computation.

关键词： Unconstrained optimization nonmonotone line search scaled conjugate gradient algorithm convergence analysis numerical comparisons

来源：评论

学校读者我要写书评

暂无评论

A note on the global convergence theorem of the scaled conjugate gradient algorithms proposed by Andrei

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2012年第2期52卷 409-414页

作者： Babaie-Kafaki, Saman Semnan Univ Dept Math Fac Math Stat & Comp Sci Semnan Iran

In (Andrei, Comput. Optim. Appl. 38:402-416, 2007), the efficient scaled conjugate gradient algorithm SCALCG is proposed for solving unconstrained optimization problems. However, due to a wrong inequality used in (Andrei, Comput. Optim. Appl. 38:402-416, 2007) to show the sufficient descent property for the search directions of SCALCG, the proof of Theorem 2, the global convergence theorem of SCALCG, is incorrect. Here, in order to complete the proof of Theorem 2 in (Andrei, Comput. Optim. Appl. 38:402-416, 2007), we show that the search directions of SCALCG satisfy the sufficient descent condition. It is remarkable that the convergence analyses in (Andrei, Optim. Methods Softw. 22:561-571, 2007;Eur. J. Oper. Res. 204:410-420, 2010) should be revised similarly.

关键词： scaled conjugate gradient algorithm Sufficient descent condition Line search Global convergence

来源：评论

学校读者我要写书评

暂无评论

Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network

引用

SOFT COMPUTING 2023年第10期27卷 6687-6702页

作者： Paul, Banibrata Karn, Bhaskar BIT Dept Comp Sci & Engn Ranchi Jharkhand India

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results;similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing.

关键词： Artificial neural network Cleveland datasets Cleveland Hungarian Statlog heart dataset Heart disease scaled conjugate gradient algorithm

来源：评论

学校读者我要写书评

暂无评论

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the conjugate gradient Method

引用

JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 2015年第3期120卷 113-128页

作者： Bernal, Javier Torres-Jimenez, Jose NIST Gaithersburg MD 20899 USA CINVESTAV Tamaulipas Informat Technol Lab Victoria Tamaulipas Mexico

SAGRAD (Simulated Annealing gradient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Moller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Moller's algorithm;the (re) initialization of weights with simulated annealing required to (re) start Moller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum;and the use of simulated annealing when Moller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

关键词： batch learning neural networks for classification scaled conjugate gradient algorithm simulated annealing

来源：评论

学校读者我要写书评

暂无评论

scaled conjugate gradient Learning for Quaternion-Valued Neural Networks

Scaled Conjugate Gradient Learning for Quaternion-Valued Neu...

引用

23rd International Conference on Neural Information Processing (ICONIP)

作者： Popa, Calin-Adrian Polytech Univ Timisoara Dept Comp & Software Engn Blvd V Parvan 2 Timisoara 300223 Romania

ISBN: (纸本)9783319466750;9783319466743

This paper presents the deduction of the scaled conjugate gradient method for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of the scaled conjugate algorithm in the real- and complex-valued cases lead to the idea of extending it to the quaternion domain, also. Experiments done using the proposed training method on time series prediction applications showed a significant performance improvement over the quaternion gradient descent and quaternion conjugate gradient algorithms.

关键词： Quaternion-valued neural networks scaled conjugate gradient algorithm Time series prediction

来源：评论

学校读者我要写书评

暂无评论

scaled conjugate gradient Learning for Complex-Valued Neural Networks 21st

Scaled Conjugate Gradient Learning for Complex-Valued Neural...

引用

21st International Conference on Soft Computing (MENDEL)

作者： Popa, Calin-Adrian Polytech Univ Timisoara Dept Comp & Software Engn Timisoara 300223 Romania

ISBN: (纸本)9783319198248;9783319198231

In this paper, we present the full deduction of the scaled conjugate gradient method for training complex-valued feedforward neural networks. Because this algorithm had better training results for the real-valued case, an extension to the complex-valued case is a natural way to enhance the performance of the complex backpropagation algorithm. The proposed method is exemplified on well-known synthetic and real-world applications, and experimental results show an improvement over the complex gradient descent algorithm.

关键词： Complex-valued neural networks scaled conjugate gradient algorithm Time series prediction

来源：评论

学校读者我要写书评

暂无评论

Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings

引用

CONSTRUCTION AND BUILDING MATERIALS 2008年第5期22卷 788-800页

作者： Caglar, Naci Elmas, Muzaffer Yaman, Zeynep Dere Saribiyik, Mehmet Sakarya Univ Dept Civil Engn TR-54187 Sakarya Turkey Sakarya Univ Technol Educ Fac TR-54187 Sakarya Turkey

The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a securer, quicker and more robust method to determine the dynamic response of buildings in 3D. For this purpose, two ANN models were proposed to estimate the fundamental periods, base shear force, base bending moments and top-floor displacement of buildings in two directions. Total moment of inertia for each storey was defined in order to avoid limitation to describe the structure due to the number of columns, shear-walls and number of bays. The same input layer was submitted to different types of ANN models for various outcomes. In the ANN models, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm was employed using a scaled conjugate gradient. ANN models were developed, trained and tested in a based MATLAB program. A training set of 150 and a validation set of 15 buildings were produced from dynamic response of different buildings. Finite Element Analysis (FEA) was used to generate training and testing set of ANN models. It was demonstrated that the neural network based approach is highly successful to determine response of buildings subjected to earthquake. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： dynamic analysis response of buildings neural networks scaled conjugate gradient algorithm

来源：评论

学校读者我要写书评

暂无评论

Air mass flow estimation of diesel engines using neural network

引用

FUEL 2014年第parta期117卷 833-838页

作者： Uzun, Abdullah Sakarya Univ Sakarya Vocat Sch TR-54187 Sakarya Turkey

Air mass management is one of major factors affecting the performance of diesel engines, where experimental studies play a significant role in the performance studies. However, the experimental studies are quite expensive and time consuming. Neural network's (NN) have been used increasingly in a variety of engineering researches. NN based model are generally developed from experimental data. The objective of the study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the effects of intercooling process on performance charged diesel engine's air intake mass flow. In this study, a NN model has been developed configured tested for this purpose. The training and test data is obtained from real experimental work delivered earlier. Further details of development of NN are also demonstrated. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Neural networks Intercooling Air mass flow scaled conjugate gradient algorithm Diesel engine

来源：评论

学校读者我要写书评

暂无评论

Neural network based approach for determining the shear strength of circular reinforced concrete columns

引用

CONSTRUCTION AND BUILDING MATERIALS 2009年第10期23卷 3225-3232页

作者： Caglar, Naci Sakarya Univ Dept Civil Engn TR-54187 Sakarya Turkey

The objective of this study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the shear strength of circular reinforced concrete columns. In the application of the NN model, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN model is developed, trained and tested through a based MATLAB program. The data used for training and testing NN model are gathered from literature. NN based model outputs are compared with ACI, ATC-32, ASCE and CALTRANS codes outcomes on the basis of the experimental results. This comparison demonstrated that the NN based model is highly successful to determine the shear strength of circular reinforced concrete columns. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Circular RC column Shear strength Neural networks scaled conjugate gradient algorithm

来源：评论

学校读者我要写书评

暂无评论

Neural-based prediction of structural failure of multistoried RC buildings

引用

STRUCTURAL ENGINEERING AND MECHANICS 2016年第3期58卷 459-473页

作者： Hore, Sirshendu Chatterjee, Sankhadeep Sarkar, Sarbartha Dey, Nilanjan Ashour, Amira S. Balas-Timar, Dana Balas, Valentina E. Hooghly Engineeing&Technol Coll Chinsurah Dept Comp Science&Engn Hyderabad Andhra Pradesh India Univ Calcutta Dept Comp Sci & Engn Kolkata India Hooghly Engn & Technol Coll Chinsurah Dept Civil Engn Hyderabad Andhra Pradesh India Techno India Coll Technol Dept Informat Technol Kolkata India Tanta Univ Fac Engn Dept Elect & Elect Commun Engn Cairo Egypt Aurel Vlaicu Univ Arad Fac Educ Sci Psychol & Social Sci Bucharest Romania Aurel Vlaicu Univ Arad Fac Engn Bucharest Romania

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

关键词： Reinforced Concrete (RC) structures structural failure Artificial Neural Network (ANN) Multilayer Perceptron Feed-Forward Network (MLP-FFN) scaled conjugate gradient algorithm cross-entropy

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：