2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献_第2页

Robust evolving cloud-based controller for a hydraulic plant 基于云的液压装置鲁棒进化控制器

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604098

P. Angelov, I. Škrjanc, S. Blažič

引用次数: 48

An agent based model of stress in the workplace 基于代理的工作场所压力模型

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604113

D. Ashlock, M. Page

引用次数: 5

Learning imbalanced classes in the presence of concept growth

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604106

Wing Yee Sit, Ke Zhi Mao

引用次数: 3

Adapting meeting tools to agent decision 使会议工具适应座席决策

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604102

João Barreto, Isabel Praça, T. Pinto, T. Sousa, Z. Vale

引用次数: 3

Controlling evaluation duration in On-line, on-board evolutionary robotics 在线、机载进化机器人的评估时间控制

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604109

A. Arif, D. Nedev, E. Haasdijk

引用次数: 3

Fuzzy decision trees for dynamic data 动态数据的模糊决策树

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604100

C. Marsala

引用次数: 10

A fuzzy-genetic tactical resource planner for workforce allocation 劳动力分配的模糊遗传战术资源规划

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604111

Ahmed Mohamed, H. Hagras, S. Shakya, G. Owusu

{"title":"A fuzzy-genetic tactical resource planner for workforce allocation","authors":"Ahmed Mohamed, H. Hagras, S. Shakya, G. Owusu","doi":"10.1109/EAIS.2013.6604111","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604111","url":null,"abstract":"For the recent few years, resource planning has become an interesting research topic for many companies, especially within telecommunications domain. Resource planning is basically trying to provide a high quality of service while trying to keep the cost as low as possible. The main aim of resource planning is to utilize the available resources as much as possible so that they can match the expected demand for services. Tactical resource planning looks at medium-term planning periods, i.e. weeks to months, and aims to establish coarse-grain resource deployments. In our previous work we introduced an experimental fuzzy based resource planning approach modeled for a delivery unit in British Telecom (BT) [1]. We presented a hierarchical based fuzzy logic system, which calculates the compatibility between resources and the allocated tasks, and then matches the most compatible tasks and resources to each other. The proposed hierarchical fuzzy logic based system (in an experimental setting) was able to achieve very good results in comparison to the original system, where the proposed system was able to achieve 12.2% improvement in tasks done per resource. In this paper, we introduce a hierarchical fuzzy logic based system that uses evolutionary systems to tune the fuzzy membership functions, which result in an improvement in the overall output of the system. The new fuzzy-genetic based system was able achieve better improvement in tasks done per resource than the hierarchical fuzzy logic based system that was tuned by experts.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130152167","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: 3

Network robustness and topological characteristics in scale-free networks 无标度网络的鲁棒性和拓扑特性

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604114

D. Kasthurirathna, Piraveenan Mahendra, Gnana Thedchanamoorthy

引用次数: 6

eVQ-AM: An extended dynamic version of evolving vector quantization eVQ-AM:演化向量量化的扩展动态版本

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604103

E. Lughofer

{"title":"eVQ-AM: An extended dynamic version of evolving vector quantization","authors":"E. Lughofer","doi":"10.1109/EAIS.2013.6604103","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604103","url":null,"abstract":"In this paper, we are presenting a new dynamically evolving clustering approach which extends conventional evolving Vector Quantization (eVQ), successfully applied before as fast learning engine for evolving cluster models, classifiers and evolving fuzzy systems in various real-world applications. The first extension concerns the ability to extract ellipsoidal prototype-based clusters in arbitrary position, thus increasing its flexibility to model any orentiation/rotation of local data clouds. The second extension includes a single-pass merging strategy in order to resolve unnecessary overlaps or to dynamically compensate inappropriately chosen learning parameters (which may lead to over-clustering effects). The new approach, termed as eVQ-AM (eVQ for Arbitrary ellipsoids with Merging functionality), is compared with conventional eVQ, other incremental and batch learning clustering methods based on two-dimensional as well as high-dimensional streaming clustering showing an evolving behavior in terms of adding/joining clusters as well as feature range expansions. The comparison includes a sensitivity analysis on the learning parameters and observations of finally achieved cluster partition qualities.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994848","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

Resolving global and local drifts in data stream regression using evolving rule-based models 使用不断发展的基于规则的模型解决数据流回归中的全局和局部漂移

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-01 DOI: 10.1109/EAIS.2013.6604099

Ammar Shaker, E. Lughofer

{"title":"Resolving global and local drifts in data stream regression using evolving rule-based models","authors":"Ammar Shaker, E. Lughofer","doi":"10.1109/EAIS.2013.6604099","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604099","url":null,"abstract":"In this paper, we present new concepts for dealing with drifts in data streams during the run of on-line modeling processes for regression problems in the context of evolving fuzzy systems. Opposed to the nominal case based on conventional life-long learning, drifts are requiring a specific treatment for the modeling phase, as they refer to changes in the underlying data distribution or target concepts, which makes older learned concepts obsolete. Our approach comes with three new stages for an appropriate drift handling: 1.) drifts are not only detected, but also quantified with a new extended version of the Page-Hinkley test, which overcomes some instabilities during downtrends of the indicator; 2.) based on the current intensity quantification of the drift, the necessary degree of forgetting (weak to strong) is extracted (adaptive forgetting); 3.) the latter is achieved by two variants, a.) a single forgetting factor value, accounting for global drifts, and b.) a forgetting factor vector with different entries for separate regions of the feature space, accounting for local drifts. Forgetting factors are integrated into the learning scheme of both, the antecedent and consequent parts of the evolving fuzzy systems. The new approach will be evaluated on high-dimensional data streams, where the results will show that 1.) our adaptive forgetting strategy outperforms the usage of fixed forgetting factors throughout the learning process and 2.) forgetting in local regions may improve forgetting in global ones when drifts appear locally.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124002721","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: 8