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2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献

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Evolving controller design for decoupled multivariate plants 解耦多变量对象的演化控制器设计
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502367
C. Bányász, L. Keviczky
{"title":"Evolving controller design for decoupled multivariate plants","authors":"C. Bányász, L. Keviczky","doi":"10.1109/EAIS.2016.7502367","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502367","url":null,"abstract":"The paper investigates how a controller can be designed in an evolving framework for multivariable linear dynamic processes extending the classical Youla-parameterization for MIMO plants.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115553192","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
Sensor drift compensation using weighted neural networks 基于加权神经网络的传感器漂移补偿
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-01 DOI: 10.1109/EAIS.2016.7502497
Thiago Wiezbicki, Eduardo Parente Ribeiro
{"title":"Sensor drift compensation using weighted neural networks","authors":"Thiago Wiezbicki, Eduardo Parente Ribeiro","doi":"10.1109/EAIS.2016.7502497","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502497","url":null,"abstract":"In gas classification systems with multiple sensors, the individual sensor drift affects the system classification capacity over time. A model created to classify data at certain time, doesn't present the same efficiency to classify a sample in a future time. Depending on the problem, this time interval can be days, weeks or months. Chemical gas sensors suffer from drift problem because of the chemical process employed. In this investigation we developed a model that uses an ensemble of neural networks in a parallel way combining the weighted output of classifiers to compensate the drift. Another approach was to weight input data according to their recentness by repeating newer training values. Results show that performance of correct classifications of the gas samples using both methods improved when compared to classifiers trained with just recent data.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116827628","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
Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective 学习不公平交易:强化学习视角下的市场操纵分析
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2015-11-02 DOI: 10.1109/EAIS.2016.7502499
E. Miranda, P. McBurney, M. Howard
{"title":"Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective","authors":"E. Miranda, P. McBurney, M. Howard","doi":"10.1109/EAIS.2016.7502499","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502499","url":null,"abstract":"Market manipulation is a strategy used by traders to alter the price of financial assets. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools have been developed to detect price manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading from a macroscopic perspective of profit maximisation, two strategies that differ in the legal background but share the same elemental concept of market manipulation. We use a reinforcement learning framework within the full and partial observability of Markov decision processes and analyse the underlying behaviour of the perpetrators by finding the causes of what encourages these traders to perform fraudulent activities. Procedures can be applied to counter the problem as our model predicts the activity of the manipulators.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541253","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: 32
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