2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)最新文献_第3页

Online self-evolving fuzzy controller for autonomous mobile robots 自主移动机器人在线自进化模糊控制器

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945918

Pouria Sadeghi-Tehran, P. Angelov

引用次数: 16

Incremental classification of process data for anomaly detection based on similarity analysis 基于相似度分析的过程数据异常检测增量分类

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945928

S. Byttner, M. Svensson, G. Vachkov

{"title":"Incremental classification of process data for anomaly detection based on similarity analysis","authors":"S. Byttner, M. Svensson, G. Vachkov","doi":"10.1109/EAIS.2011.5945928","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945928","url":null,"abstract":"Performance evaluation and anomaly detection in complex systems are time consuming tasks based on analyzing, similarity analysis and classification of many different data sets from real operations. This paper presents an original computational technology for unsupervised incremental classification of large data sets by using a specially introduced similarity analysis method. First of all the so called compressed data models are obtained from the original large data sets by a newly proposed sequential clustering algorithm. Then the data sets are compared by pairs not directly, but by using their respective compressed data models. The evaluation of the pairs is done by a special similarity analysis method that uses the so called Intelligent Sensors (Agents) and data potentials. Finally a classification decision is generated by using a predefined threshold of similarity. The applicability of the proposed computational scheme for anomaly detection, based on many available large data sets is demonstrated on an example of 18 synthetic data sets. Suggestions for further improvements of the whole computation technology and a better applicability are also discussed in the paper.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132788946","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

Artificial immune system-based classification in class-imbalanced problems 基于人工免疫系统的分类中的类不平衡问题

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945917

Dionisios N. Sotiropoulos, G. Tsihrintzis

引用次数: 6

Tackling uncertainties in self-optimizing systems by strategy blending 用策略混合处理自优化系统中的不确定性

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945920

N. Rosemann, W. Brockmann, Rolf Thomas Hänel

{"title":"Tackling uncertainties in self-optimizing systems by strategy blending","authors":"N. Rosemann, W. Brockmann, Rolf Thomas Hänel","doi":"10.1109/EAIS.2011.5945920","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945920","url":null,"abstract":"Complex technical applications often show severe uncertainties, which may vary over time, e.g., situation dependent sensor inaccuracies or anomalies and faults. In order to ease the engineering process for such systems, organic computing principles, e.g., self-adaptation and self-optimization, offer a solution. Hence, machine learning paradigms are needed which work online and which can cope with such dynamically varying uncertainties, but still operate safely all the time. In this work, such a learning paradigm is developed based on the Organic Robot Control Architecture and the incremental learning scheme Directed Self-Learning. It is combined with an explicit uncertainty representation. The core idea is to use a strategy blending scheme to show a good performance and improve it by self-optimizing learning on the one hand in case of high trust, or low uncertainty, respectively. On the other hand, a robust fallback-system is used to ensure safety in situations of high uncertainty. Of course, in such situations learned knowledge has to be protected from corruption. The feasibility of this approach is demonstrated in a simulated pick-and-place scenario with unknown, but changing load masses.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131177014","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: 0