2006 IEEE Conference on Cybernetics and Intelligent Systems最新文献_第10页

An Efficient MPC Algorithm based on a Priori Zone Control 基于先验区域控制的高效MPC算法

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252347

P. Park, S. Kim, J. Moon, M. Shin

引用次数: 0

Mean Values of Fuzzy Numbers and the Measurement of Fuzziness by Evaluation Measures 模糊数的均值与评价指标对模糊性的度量

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252245

Y. Yoshida

引用次数: 7

OWL/XDD: A Formal Language for Application Profiles OWL/XDD:一种用于应用程序概要的形式化语言

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252313

Photchanan Ratanajaipan, E. Nantajeewarawat, V. Wuwongse

引用次数: 2

Adaptive Language Independent Spell Checking using Intelligent Traverse on a Tree 在树上使用智能遍历自适应语言独立拼写检查

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252325

Behrang Q. Zadeh, A. Ilkhani, A. Ganjeii

引用次数: 11

A Forecasting Model of Dynamic Grey Rough Set and its Application on Stock Selection 动态灰色粗糙集预测模型及其在选股中的应用

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252320

Ting-Cheng Chang, Chuen-Jiuan Jane, Yuan-Paio Lee

{"title":"A Forecasting Model of Dynamic Grey Rough Set and its Application on Stock Selection","authors":"Ting-Cheng Chang, Chuen-Jiuan Jane, Yuan-Paio Lee","doi":"10.1109/ICCIS.2006.252320","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252320","url":null,"abstract":"The main purpose of paper is to establish a system, which combines rough set and grey theory. This model is used to let the time-serial, season-serial or regular data have the dynamic trend concepts by grey prediction, then, select the data sets with trend value through rough set screening system. It mainly is applied for a portfolio prediction in the stock market. Our study first predicts each listed company's attributes of condition and decision-making by grey prediction, secondly groups their attributes by K-means grouping tools, then filters and categorizes the groups with the classified capacity of rough set for uncertain and non-sufficient information and selects the stock portfolio. And then we evaluate the company shares from the portfolio according to their past EPS and ROE and elect the better ones again. Finally, the selected companies are arranged in order with grey relation and determine the weight of each share in the portfolio according to it. The experimental result in Taiwan: during five years (2000-2004), the average annual rate of return was 38.1%. The portfolio determined by the model overran the market dramatically","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"48 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060099","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: 1

Linguistic Knowledge Extraction from Neural Networks Using Maximum Weight and Frequency Data Representation 基于最大权值和频率数据表示的神经网络语言知识提取

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252314

W. Wettayaprasit, U. Sangket

引用次数: 7

Particle Swarm Assisted Incremental Evolution Strategy for Function Optimization 粒子群辅助的函数优化增量进化策略

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252276

W. Mo, S. Guan

{"title":"Particle Swarm Assisted Incremental Evolution Strategy for Function Optimization","authors":"W. Mo, S. Guan","doi":"10.1109/ICCIS.2006.252276","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252276","url":null,"abstract":"This paper presents a new evolutionary approach for function optimization problems particle swarm assisted incremental evolution strategy (PIES). Two strategies are proposed. One is incremental optimization that the whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, a population is evolved with respect to one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the population obtained by the SVE in current phase and by the MVE in the last phase. And then the MVE is taken on the incremented variable set. The second strategy is a hybrid of particle swarm optimization (PSO) and the evolution strategy (ES). PSO is applied to adjust the cutting planes (in SVEs) or hyper-planes (in MVEs) while ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that PIES generally outperforms three other evolutionary algorithms, improved normal GA, PSO and SADEXERAF, in the sense that PIES finds solutions with more optimal objective values and closer to the true optima","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125145324","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: 1

A Robust Algorithm for Classification Using Decision Trees 基于决策树的鲁棒分类算法

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252336

B. Chandra, P. Paul V

引用次数: 13

Multi-criteria Intelligent Dispatching Control of Automated Guided Vehicles in FMS FMS中自动引导车辆的多准则智能调度控制

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252292

N. Umashankar, V. Karthik

引用次数: 10

Pre-Eliminating Features for Fast Training in Real Time Object Detection in Images with a Novel Variant of AdaBoost AdaBoost的一种新变体，用于图像中实时目标检测的快速训练的预消除功能

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252285

M. Stojmenovic

{"title":"Pre-Eliminating Features for Fast Training in Real Time Object Detection in Images with a Novel Variant of AdaBoost","authors":"M. Stojmenovic","doi":"10.1109/ICCIS.2006.252285","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252285","url":null,"abstract":"Our primary interest is to build fast and reliable object recognizers in images based on small training sets. This is important in cases where the training set needs to be built mostly manually, as in the case that we studied, the recognition of the Honda Accord 2004 from rear views. We described a novel variant of the AdaBoost based learning algorithm, which builds a strong classifier by incremental addition of weak classifiers (WCs) that minimize the combined error of the already selected WCs. Each WC is trained only once, and examples do not change their weights. We proposed to pre-eliminate features whose cumulative error of corresponding best WCs exceeds a predetermined threshold value. We tested two straightforward definitions of cumulative error. In both cases, we showed that, when over 97% of the initial features are eliminated at the very beginning from further training, training time is drastically reduced while having little impact on the quality of the pool of available WCs. This is a novel method that has reduced the training set WC quantity to less than 3% of its original number, greatly speeding up training time, and showing no negative impact on the quality of the final classifier. Our experiments indicated that the set of features used by Viola and Jones and others for face recognition was inefficient for our problem; therefore, each object requires its own custom-made set of features for real time and accurate recognition. Our training method, combined with the selection of appropriate features, has resulted in finding a very accurate classifier containing merely 30 weak classifiers. Compared to existing literature, we have overall achieved the design of a real time object detection machine with the least number of examples, the least number of weak classifiers, the fastest training time, and with competitive detection and false positive rates","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314138","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: 4