检索结果-内蒙古大学图书馆

Existence of optimal weighted least squares estimate for three-parametric exponential model

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2008年第9期37卷 1383-1398页

作者： Dubeau, Francois Mir, Youness Univ Sherbrooke Dept Math Sherbrooke PQ J1K 2R1 Canada

Given a set of data {(omega(i), t(i), f(i))}(i=1)(m), we present sufficient conditions which guarantee the existence of the weighted least squares estimate for a three-parametric exponential function. To this end, we suggest a classification of the data based on their increasing or decreasing trend, and on their convexity or concavity form. We discuss a choice of the initial approximation and present a numerical example.

关键词： classification of data existence problem least squares estimate parametric exponential function weighted least squares

来源：评论

学校读者我要写书评

暂无评论

Probabilistic simplified fuzzy ARTMAP (PSFAM)

引用

IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY 1999年第4期146卷 165-169页

作者： Jervis, BW Garcia, T Giahnakis, EP Sheffield Hallam Univ Sch Engn Sheffield S1 1WB S Yorkshire England Univ La Rochelle Dept Comp Sci La Rochelle France Univ Crete Dept Math Iraklion 71409 Crete Greece

The probabilistic simplified fuzzy ARTMAP (PSFAM) has been developed for fast training and offline or online learning and classification of data together with a probability measure of confidence in the classification. A simplified fuzzy ARTMAP (SFAM) and a Bayes' classifier are combined. Using a committee of SFAMs and brain-evoked response data from four groups of subjects, classification accuracies in the range 87-97%;are achieved together with ideal or near-ideal medical statistics. The limitations appeared to be associated with the data.

关键词： fast training Learning in AI inference mechanisms probability measure of confidence offline learning medical expert systems fuzzy neural nets prediction errors Pattern recognition classification of data brain-evoked response data Neural nets belief networks Systems theory applications in biology and medicine ideal medical statistics Bayes classifier biosignal data pattern classification ART neural nets learning (artificial intelligence) probabilistic simplified fuzzy ARTMAP Reasoning and inference in AI online learning

来源：评论

学校读者我要写书评

暂无评论

A dynamic two-level artificial neural network for estimation of parameters in combined mode conduction-radiation heat transfer in porous medium: An application to handle huge dataset with noise

引用

HEAT TRANSFER 2022年第2期51卷 1306-1335页

作者： Mishra, Vijay K. Dasgupta, Udrek Patra, Sushmit Pal, Rohan Anand, Kshitij Kalinga Inst Ind Technol Dept Mech Engn Bhubaneswar 751024 India

A dynamic two-level artificial neural network (DTLANN) approach is used for the estimation of parameters in combined mode conduction-radiation heat transfer in a porous medium. Four commonly used neural networks: feed forward, cascade forward, fitnet, and radial basis are used in mapping artificial neural network (ANN), and their performance is compared under noisy big data (10,302 x 1300 matrix size). Governing equations for heat transfer in the porous medium through conduction and radiation modes are solved by finite volume method and discrete transfer method. This numerical model is called a direct model. A large amount of data is generated by using the direct model for different values of extinction coefficient beta and convective coupling P-2. These data were divided into different groups (class) based on the temperature difference between the gas and solid phase. In the inverse analysis, a new pair of temperature profiles for the solid and gas phase is taken as input and classified with the help of a pattern net artificial neural network model. On the basis of classification, data from that particular class and its neighbor class are used for training the mapping ANN model. After the training of the mapping ANN model, corresponding values of beta and P-2 are obtained as output for any new input. This DTLANN model has a high regression coefficient (R) of .99131 and can predict highly accurate values of parameters under a huge dataset with noise, within much less CPU time.

关键词： artificial neural network classification of data conductive and radiative transfer finite volume method inverse analysis parameter estimations

来源：评论

学校读者我要写书评

暂无评论

Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

引用

HEAT TRANSFER 2022年第6期51卷 5386-5416页

作者： Mishra, Vijay K. Anand, Kshitij Bhardwaj, Abhishek Kalinga Inst Ind Technol Dept Mech Engn Bhubaneswar 751024 India

A pattern net assisted mapping artificial neural network (PAMANN) model for estimation of parameters in problem with large data (1300 x 121 matrix size) is reported. A pattern net-based multilayer perceptron neural network (MLPNN) model for clustering the data, followed by mapping MLPNN model for mapping the target with the input, is developed as PAMANN model. A heat transfer problem with combined mode conduction and radiation in porous medium is solved numerically, and is called direct model. In the inverse model, a PAMANN model is developed by using data generated through the direct model. The PAMANN model is able to estimate two parameters (extinction coefficient beta and convective coupling P-2) after taking temperature profile as input. The model is tested for different number of neurons in hidden layer, and different levels of noise in input data. Twelve different algorithms are explored in training of mapping MLPNN, and compared for performance. Levenberg-Marquardt algorithm is found to estimate the parameters with high accuracy, but took high CPU time. Bayesian regularization is found to consume very high CPU time with moderate accuracy in estimation of parameters. Variations in hidden layer neuron number and noise in input data, were done to analyze the performance of mapping MLPNN with different training algorithms. Algorithms O-Step Secant, conjugate gradient with Polak-Ribiere updates, and conjugate gradient with Fletcher-Reeves updates are able to handle all variations of noise and number of neurons in hidden layer, with good accuracy of estimation and low CPU time consumption. Under high computational resource LM algorithm can be used for all cases. Up to 0.99132 value of regression coefficient is obtained in mapping MLPNN model with 15 hidden neurons, indicating the high accuracy of the model. With the help of PAMANN model, highly accurate (absolute error 1.78%) estimation of parameters is obtained. The model can handle upto 1% noise in input data, wh

关键词： artificial neural network classification of data conductive and radiative transfer finite volume method inverse analysis parameter estimations

来源：评论

学校读者我要写书评

暂无评论

Interactive Voice Response Field Classifiers

Interactive Voice Response Field Classifiers

引用

IEEE International Conference on System, Man, and Cybernetic

作者： Patel, P. B. Marwala, T. Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa

ISBN: (纸本)9781424423835

Accurate classification of caller interaction within Interactive Voice Response systems would assist corporations to determine caller behaviour within these solutions. This paper proposes an application, which employs artificial neural networks that could assist contact centers to determine caller activity within their automated systems. Multi-layer perceptron and Radial Basis Function neural network architectures are implemented as classifiers to determine caller interaction. Field classifiers for a pay beneficiary application were developed. 'Say account' networks were created utilizing 'generated' and 'live' data sources. Multi-layer perceptron networks proved appropriate for this application. The most accurate network created, 99.99%, is the 'Say account' classifier. The difference in accuracy between the 'generated' and 'live' classifiers is approximately 2%. However, greater development effort is required to implement the former. As a result, the 'live' data source methodology is preferred.

关键词： neural networks classification of data contact center analytics Interactive Voice Response Voice Extensible Markup Language

来源：评论

学校读者我要写书评

暂无评论

Natural versus Granular Computing: Classifiers from Granular Structures

引用

6th International Conference on Rough Sets and Current Trends in Computing

作者： Artiemjew, Piotr Univ Warmia & Mazury Olsztyn Poland

ISBN: (纸本)9783540884231

In data sets/decision systems, written down as pairs (U, A boolean OR {d}) with objects U, attributes A;and a decision d;objects are described in terms of attribute-value formulas. This representation gives rise to a calculus in terms of descriptors which we call a natural computing. In some recent papers;the idea of L. Polkowski of computing with granules induced from similarity measures called rough inclusions have been tested. In this work, we pursue this topic and we study granular structures resulting from rough inclusions with classification problem in focus. Our results show that classifiers obtained from granular structures give better quality of classification than natural exhaustive classifiers.

关键词： rough inclusions granular computing classification of data

来源：评论

学校读者我要写书评

暂无评论

The Application of decision tree in security audit

The Application of decision tree in security audit

引用

3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Dan, Shao Bai, Wang Yan Changchun Univ Dept Comp Changchun Peoples R China

ISBN: (纸本)9783037858431

Network security audit is an important part in network security system, which, to some extent, makes up for the deficiencies in firewalls and intrusion detections. According to the characteristics of the network data, the traditional decision tree model is modified, and the application pattern in which the decision tree model is used to network data security audit is also proposed. Simulations show that the decision tree classification model not only is adaptive to the characteristics of network data, but also can prevent malicious attacks effectively. The effects of the attributes such as IP, port, and protocol type on classification are also uncovered.

关键词： Network data Security audit Decision tree algorithm classification of data

来源：评论

学校读者我要写书评

暂无评论

The Improvement of C4.5 Algorithm and Case Study

The Improvement of C4.5 Algorithm and Case Study

引用

2nd International Symposium on Computational Intelligence and Design

作者： Li Rui Wei Xian-mei Yu Xue-wei Dalian Jiaotong Univ Software Inst Dalian Peoples R China

ISBN: (纸本)9780769538655

The paper introduces a balanced coefficient to improve the veracity of C4.5 algorithm. It can be fixed by decision maker according to priori intellectual and domain intellectual. It harmonized the information gain-ratio of each attributes artificially in specific environment. The classification is more veracious and rational by the decision tree made from the improved algorithm. And compared the improved algorithm to C4.5 algorithm by analyzing examples, to prove the efficiency of the improved algorithm.

关键词： classification of data Decision tree C4.5 algorithm Degree of balance coefficient

来源：评论

学校读者我要写书评

暂无评论

Ensemble of Classifiers Based on Simple Granules of Knowledge 23rd

Ensemble of Classifiers Based on Simple Granules of Knowledg...

引用

23rd International Conference on Information and Software Technologies (ICIST)

作者： Artiemjew, Piotr Univ Warmia & Mazury Fac Math & Comp Sci Sloneczna 54 PL-10710 Olsztyn Poland

ISBN: (纸本)9783319676425;9783319676418

The idea of classification based on simple granules of knowledge (CSG classifier) is inspired by granular structures proposed by Polkowski. The simple granular classifier turned up to be really effective in the context of real data classification. Classifier among others turned out to be resistant for damages and can absorb missing values. In this work we have presented the continuation of series of experimentations with boosting of rough set classifiers. In the previous works we have proven effectiveness of pair and weighted voting classifier in mentioned context. In this work we have checked a few methods for classifier stabilization in the context of CSG classifier - Bootstrap Ensemble (Simple Bagging), Boosting based on Arcing, and Ada-Boost with Monte Carlo split. We have performed experiments on selected data from the UCI Repository. The results show that the committee of simple granular classifiers stabilized the classification process. Simple Bagging turned out to be most effective for CSG classifier.

关键词： Rough sets Rough inclusions Granules of knowledge classification of data Bootstrap ensemble Ada-Boost Arcing

来源：评论

学校读者我要写书评

暂无评论

A new fuzzy time series forecasting method based on clustering and weighted average approach

A new fuzzy time series forecasting method based on clusteri...

引用

Journal of Intelligent and Fuzzy Systems

作者： Iqbal, Shafqat Zhang, Chongqi Arif, Muhammad Hassan, Munawar Ahmad, Shakeel School of Economics and Statistics Guangzhou University Guangzhou China School of Computer Science and Technology Guangzhou University Guangzhou China College of Agriculture Economics and Management Jilin Agriculture University China

Time series is a classification of data series of variables, orderly arranged with respect to time. In time series analysis, forecasting is the vital area of study besides other meaningful characteristics of the data. It has vast application in decision-making and prediction in the domain of economics, agriculture, medicine, industries, energy sector and other sciences. Fuzzy time series emerged as a robust tool to cater for historical data in linguistic values. This paper proposes the new method of fuzzy time series forecasting based on the approach of fuzzy clustering and information granules integrated with the weighted average approach to deal with the uncertainty in data series. To distinguish the power of modeling and prediction, the strategies of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized as a criterion. Findings illustrate that proposed fuzzy based time series approach is vigorous to compute the accurate estimates. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Time series analysis Agricultural robots classification (of information) Decision making Forecasting Fuzzy neural networks Information granules Mean square error classification of data Fuzzy time series Historical data Linguistic values Mean absolute error Modeling and predictions Root mean squared errors Weighted averages

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：