IEEE International Conference on Granular Computing最新文献_第2页

A new classification algorithm based on ensemble PSO_SVM and clustering analysis 基于集成PSO_SVM和聚类分析的分类算法

IEEE International Conference on Granular Computing Pub Date : 2012-08-11 DOI: 10.1109/GrC.2012.6468652

T. Zhou, Huiling Lu, Lihua Liu, Longquan Yong, Shouheng Tuo

引用次数: 1

Qualitative mapping defined wavelet transformation 定性映射定义了小波变换

IEEE International Conference on Granular Computing Pub Date : 2012-08-11 DOI: 10.1109/GrC.2012.6468705

Jia-li Feng

引用次数: 0

Text-continuous speech recognition based on ICA and geometrical learning 基于ICA和几何学习的文本连续语音识别

IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635877

Wenming Cao, Tiancheng He, Shoujue Wang

引用次数: 0

New Soft Sensor Method Based on SVM 基于支持向量机的软测量新方法

IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI: 10.1109/GRC.2006.1635861

Haoran Zhang, Xiaodong Wang, Changjiang Zhang, G. Lv

{"title":"New Soft Sensor Method Based on SVM","authors":"Haoran Zhang, Xiaodong Wang, Changjiang Zhang, G. Lv","doi":"10.1109/GRC.2006.1635861","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635861","url":null,"abstract":"This paper proposes a soft sensor technique based on support vector machine(SVM) technique, firstly gives an introduction to LSSVM, then designs a training algorithm for LSSVM, finally uses it to identify Absorption Stabilization System (ASS) process variable. Case studies are performed and indicate that the proposed method provides satisfactory performance with excellent approximation and generalization property, soft sensor technique based on LSSVM achieves superior performance to the conventional method based on neural networks. approaches. The formulation of the SVM embodies the Structural Risk Minimization (SRM) principle, which has been shown to be superior to the traditional Empirical Risk Minimization (ERM) principle, employed in conventional neural networks. It is this difference that equips SVM with a greater ability to generalize, hence a better generalization ability is guaranteed. As an interesting variant of the standard support vector machines, least squares support vector machines (LSSVM) have been proposed by Suykens and Vandewalle(5,6) for solving pattern recognition and nonlinear function estimation problems. Standard SVM formulation is modified in the sense of ridge regression and taking equality instead of inequality constraints in the problem formulation. As a result one solves a linear system instead of a QP problem, so LSSVM is easy to training. This paper discusses the basic principle of the LSSVM at first, and then uses it as a soft sensor tool to identify Absorption Stabilization System (ASS) process variable. The method can achieve higher identification precision at reasonably small size of training sample set and can overcome disadvantages of the artificial neural networks (ANNs). The experiments of the identification have been presented and discussed. The results indicate that the SVM method exhibits good generalization performance.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114479665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Toward a generalized theory of uncertainty (GTU) - an outline 走向广义不确定性理论(GTU)——概述

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547227

L. Zadeh

引用次数: 628

Protein structure prediction and understanding using machine learning methods 使用机器学习方法预测和理解蛋白质结构

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547225

Yi Pan

{"title":"Protein structure prediction and understanding using machine learning methods","authors":"Yi Pan","doi":"10.1109/GRC.2005.1547225","DOIUrl":"https://doi.org/10.1109/GRC.2005.1547225","url":null,"abstract":"Summary form only given. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein structures from protein sequences using computer programs is an important step to unveil proteins' three dimensional conformation and functions. As a result, prediction of protein structures has profound theoretical and practical influence over biological study. In this talk, we would show how to use machine learning methods with various advanced encoding schemes and classifiers improve the accuracy of protein structure prediction. The explanation of how a decision is made is also important for improving protein structure prediction. The reasonable interpretation is not only useful to guide the \"wet experiments\", but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. Some preliminary results using SVM and decision tree for rule extraction and prediction interpretation would also be presented.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125197032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Intelligent business operation management 智能企业运营管理

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547226

M. Shan

引用次数: 3

Optimal policies in multistage fuzzy control information granulation and interpolative reasoning 多级模糊控制信息粒化与插值推理的最优策略

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547223

J. Kacprzyk

引用次数: 0

Mechanism approach to a unified theory of artificial intelligence 人工智能统一理论的机制方法

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547228

Y. Zhong

引用次数: 1

Difference between data mining and knowledge discovery - a view to discovery from knowledge-processing 数据挖掘与知识发现的区别——从知识处理看知识发现

IEEE International Conference on Granular Computing Pub Date : 2005-07-25 DOI: 10.1109/GRC.2005.1547224

S. Ohsuga

引用次数: 1