检索结果-内蒙古大学图书馆

negative correlation learning in the extreme learning machine framework

NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13805-13823页

作者： Perales-Gonzalez, Carlos Carbonero-Ruz, Mariano Perez-Rodriguez, Javier Becerra-Alonso, David Fernandez-Navarro, Francisco Univ Loyola Andalucia Seville Spain

Extreme learning machine (ELM) has shown to be a suitable algorithm for classification problems. Several ensemble meta-algorithms have been developed in order to generalize the results of ELM models. Ensemble approaches introduced in the ELM literature mainly come from boosting and bagging frameworks. The generalization of these methods relies on data sampling procedures, under the assumption that training data are heterogeneously enough to set up diverse base learners. The proposed ELM ensemble model overcomes this strong assumption by using the negative correlation learning (NCL) framework. An alternative diversity metric based on the orthogonality of the outputs is proposed. The error function formulation allows us to develop an analytical solution to the parameters of the ELM base learners, which significantly reduce the computational burden of the standard NCL ensemble method. The proposed ensemble method has been validated by an experimental study with a variety of benchmark datasets, comparing it with the existing ensemble methods in ELM. Finally, the proposed method statistically outperforms the comparison ensemble methods in accuracy, also reporting a competitive computational burden (specially if compared to the baseline NCL-inspired method).

关键词： negative correlation learning Extreme learning machine Ensemble Diversity

来源：评论

学校读者我要写书评

暂无评论

negative correlation learning in the Estimation of Distribution Algorithms for Combinatorial Optimization

引用

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第11期E96D卷 2397-2408页

作者： Wattanapornprom, Warin Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of Estimation of Distribution Algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

关键词： combinatorial optimization estimation of distribution algorithms negative correlation learning multimodal

来源：评论

学校读者我要写书评

暂无评论

negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting

引用

RENEWABLE ENERGY 2020年 156卷 804-819页

作者： Peng, Tian Zhang, Chu Zhou, Jianzhong Nazir, Muhammad Shahzad Huaiyin Inst Technol Coll Automat Huaian 223003 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

Accurate and reliable wind speed forecasting is vital in power system scheduling and management. Ensemble techniques are widely employed to enhance wind speed forecasting accuracy. This paper proposes a negative correlation learning-based regularized extreme learning machine ensemble model (NCL-RELM) integrated with optimal variational mode decomposition (OVMD) and sample entropy (SampEn) for multi-step ahead wind speed forecasting. For this purpose, the original wind speed time series is firstly decomposed into a few variational modes and a residue using OVMD, and then the decomposed subseries with approximate SampEn values are aggregated into a new subseries to reduce the computational burden. Secondly, a NCL-RELM ensemble model is employed to model each aggregated subseries. The NCL technique is employed to enhance the diversity among multiple sub-RELM models such that the predictability of a single RELM model can be enhanced. Finally, the prediction results of all subseries are added up to obtain an aggregated result for the original wind speed. The simulation results indicate that: (1) the NCL-RELM model performs better than other ensemble approaches including BAGTREE, BOOST and random forest;(2) the proposed OS-NCL-RELM model obtains the best statistical metrics from 1- to 3-step ahead forecasting compared with the other nine benchmark models. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wind speed forecasting Regularized extreme learning machine negative correlation learning Optimal variational mode decomposition Sample entropy

来源：评论

学校读者我要写书评

暂无评论

negative correlation learning approach for T-S fuzzy models

Negative correlation learning approach for T-S fuzzy models

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Cai, YP Sun, XM Jia, PF Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)0780385667

In this paper an adaptive approach of achieving a proper model structure in data-driven T-S fuzzy models is proposed. By introducing negative correlation learning in the creation of the fuzzy model, the training error of the entire model is decomposed to individual rule errors with correlation penalty term. Fuzzy rules can be trained and evaluated separately. On the other hand, negative correlation learning minimizes the mutual information between rules, so that a set of cooperative and complementary fuzzy rules can be obtained. The correlation penalty term also provides a way of measuring the validity of each rule. Algorithms of generating and eliminating rules can be developed based on it, thus the appropriate structure of the model can be obtained independent to the initial number of rules.

关键词： fuzzy modeling negative correlation learning data-driven modeling

来源：评论

学校读者我要写书评

暂无评论

Semisupervised negative correlation learning

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2018年第11期29卷 5366-5379页

作者： Chen, Huanhuan Jiang, Bingbing Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

negative correlation learning (NCL) is an ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual ensemble member. Each ensemble member minimizes its mean square error and its error correlation with the rest of the ensemble. This paper analyzes NCL and reveals that adopting a negative correlation term for unlabeled data is beneficial to improving the model performance in the semisupervised learning (SSL) setting. We then propose a novel SSL algorithm, Semisupervised NCL (SemiNCL) algorithm. The algorithm considers the negative correlation terms for both labeled and unlabeled data for the semisupervised problems. In order to reduce the computational and memory complexity, an accelerated SemiNCL is derived from the distributed least square algorithm. In addition, we have derived a bound for two parameters in SemiNCL based on an analysis of the Hessian matrix of the error function. The new algorithm is evaluated by extensive experiments with various ratios of labeled and unlabeled training data. Comparisons with other state-of-the-art supervised and semisupervised algorithms confirm that SemiNCL achieves the best overall performance.

关键词： Committee machines ensemble learning multiple classifiers negative correlation learning semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary ensembles with negative correlation learning

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2000年第4期4卷 380-387页

作者： Liu, Y Yao, X Higuchi, T Univ Aizu Fukushima 9658580 Japan Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Div Comp Sci Electrotech Lab Evolvavble Syst Lab Tsukuba Ibaraki 3058568 Japan

Based on negative correlation learning and evolutionary learning, this brief paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.

关键词： evolutionary ensembles negative correlation learning neural networks

来源：评论

学校读者我要写书评

暂无评论

Hybrid modeling for the prediction of leaching rate in leaching process based on negative correlation learning bagging ensemble algorithm

引用

COMPUTERS & CHEMICAL ENGINEERING 2011年第12期35卷 2611-2617页

作者： Hu, Guanghao Mao, Zhizhong He, Dakuo Yang, Fei Northeastern Univ Sch Informat Sci & Engn Shenyang 11004 Peoples R China

For predicting the leaching rate in hydrometallurgical process, it is very necessary to use an accurate mathematical model in leaching process. In this paper, a mechanism model is proposed for description and analysis of heat-stirring-acid leaching process. Due to some modeling errors existed between mechanism model and actual system, a hybrid model composed of mechanism model and error compensation model is established. A new support vector regression (SVR) bagging ensemble algorithm based on negative correlation learning (NCL) is investigated for solving the problem of error compensation. The sample of the next component learner is rebuilt continuously with this algorithm to improve the ensemble errors, and the optimum ensemble result also can be obtained. Simulation results indicate that the proposed hybrid model with the new algorithm has a better prediction performance in leaching process than other models. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Leaching process Hybrid model negative correlation learning Support vector regression Simulation

来源：评论

学校读者我要写书评

暂无评论

A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble

引用

NEUROCOMPUTING 2017年 247卷 173-182页

作者： Sheng, Weiguo Shan, Pengxiao Chen, Shengyong Liu, Yurong Alsaadi, Fuad E. Hangzhou Normal Univ Dept Comp Sci Hangzhou 310036 Zhejiang Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Tianjin Univ Technol Coll Comp Sci Tianjin 300384 Peoples R China Yangzhou Univ Dept Math Yangzhou 225002 Jiangsu Peoples R China King Abdulaziz Univ Fac Engn Commun Syst & Networks CSN Res Grp Jeddah 21589 Saudi Arabia

This paper proposes a niching evolutionary algorithm with adaptive negative correlation learning, denoted as NEAJNCL, for training the neural network ensemble. In the proposed NEA_ANCL, an adaptive negative correlation learning, in which the penalty coefficient A is set to dynamically change during training, has been developed. The adaptation strategy is based on a novel population diversity measure with the purpose of appropriately controlling the trade-off between the diversity and accuracy in the ensemble. Further, a modified dynamical fitness sharing method is applied to preserve the diversity of population during training. The proposed NEA_ANCL has been evaluated on a number of benchmark problems and compared with related ensemble learning algorithms. The results show that our method can be used to design a satisfactory NN ensemble and outperform related works. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Neural network ensemble Evolutionary algorithm negative correlation learning Adaptation strategy Diversity measure

来源：评论

学校读者我要写书评

暂无评论

Combining features of negative correlation learning with mixture of experts in proposed ensemble methods

引用

APPLIED SOFT COMPUTING 2012年第11期12卷 3539-3551页

作者： Masoudnia, Saeed Ebrahimpour, Reza Arani, Seyed Ali Asghar Abbaszadeh Univ Tehran Sch Math Stat & Comp Sci Tehran Iran Shahid Rajaee Teacher Training Univ Dept Elect & Comp Engn Brain & Intelligent Syst Res Lab Tehran Iran

Both theoretical and experimental studies have shown that combining accurate neural networks (NNs) in the ensemble with negative error correlation greatly improves their generalization abilities. negative correlation learning (NCL) and mixture of experts (ME), two popular combining methods, each employ different special error functions for the simultaneous training of NNs to produce negatively correlated NNs. In this paper, we review the properties of the NCL and ME methods, discussing their advantages and disadvantages. Characterization of both methods showed that they have different but complementary features, so if a hybrid system can be designed to include features of both NCL and ME, it may be better than each of its basis approaches. In this study, two approaches are proposed to combine the features of both methods in order to solve the weaknesses of one method with the strength of the other method, i.e., gated-NCL (G-NCL) and mixture of negatively correlated experts (MNCE). In the first approach, G-NCL, a dynamic combiner of ME is used to combine the outputs of base experts in the NCL method. The suggested combiner method provides an efficient tool to evaluate and combine the NCL experts by the weights estimated dynamically from the inputs based on the different competences of each expert regarding different parts of the problem. In the second approach, MNCE, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables the training algorithm of ME to efficiently adjust the measure of negative correlation between the experts. This control parameter can be regarded as a regularization term added to the error function of ME to establish better balance in bias-variance-covariance trade-offs and thus improves the generalization ability. The two proposed hybrid ensemble methods, G-NCL and MNCE, are compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show tha

关键词： Neural networks ensemble Mixture of experts negative correlation learning

来源：评论

学校读者我要写书评

暂无评论

Design of an ensemble neural network to improve the identification performance of a gas sweetening plant using the negative correlation learning and genetic algorithm

引用

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2014年 21卷 26-39页

作者： Azizkhani, Javad Sadeghi Jazayeri-Rad, Hooshang Nabhani, Nader Petr Univ Technol Dept Automat & Instrumentat Ahvaz Iran Petr Univ Technol Dept Mech Engn Ahvaz Iran

This paper presents a combination of negative correlation learning (NCL) and Genetic Algorithm (GA) to create an ensemble neural network (ENN). In this approach the component neural networks (CNNs) of ENN are trained simultaneously. The resulting CNNs negatively correlate together through the penalty terms in their objective functions. The predicted output is obtained by using the weighted averaging of the outputs of CNNs. GA participates in the training of CNNs and assigns proper weights to each trained CNN in the ensemble. The proposed method was tested on a case study in the Gas Treatment Plant (GTP) of the AMMAK project in the Ahwaz onshore field in Iran. The testing results of the model properly follow the experimental data. In addition, the proposed method outperformed the single neural network and some other network ensemble techniques. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ensemble neural network negative correlation learning Genetic Algorithm Gas sweetening H2S content Alkanoamine

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：