2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献_第2页

A new evolving clustering algorithm for online data streams 一种新的在线数据流进化聚类算法

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502508

C. G. Bezerra, B. Costa, L. A. Guedes, P. Angelov

{"title":"A new evolving clustering algorithm for online data streams","authors":"C. G. Bezerra, B. Costa, L. A. Guedes, P. Angelov","doi":"10.1109/EAIS.2016.7502508","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502508","url":null,"abstract":"In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to “start from scratch” (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"48 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":"126261408","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: 26

Monitoring of a thermoelectric power plant based on multivariate statistical process control 基于多元统计过程控制的热电厂监测

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502371

Joyce M. F. Fonseca, Bruno M. Sousa, Webber E. Aguiar, A. R. Braga, A. Lemos, Hugo C. C. Michel, C. Braga

引用次数: 2

Model-based optimization of in-silico fed-batch ethanol production process using genetic algorithm 基于遗传算法的硅料间歇乙醇生产过程模型优化

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502506

H. F. S. Freitas, C. Andrade

引用次数: 0

Performance evaluation of the maximum correntropy criterion in identification systems 辨识系统中最大熵准则的性能评价

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502500

João P. F. Guimarães, A. I. R. Fontes, Joilson B. A. Rego, L. Silveira, A. Martins

引用次数: 5

Evolving fuzzy model for short-term prediction of energy consumption profiles 能源消费动态短期预测的演化模糊模型

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502498

D. Dovžan, I. Škrjanc

引用次数: 2

Evolving possibilistic fuzzy modeling for equity options pricing 股票期权定价的演化可能性模糊模型

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502372

Leandro Maciel, R. Ballini, F. Gomide

{"title":"Evolving possibilistic fuzzy modeling for equity options pricing","authors":"Leandro Maciel, R. Ballini, F. Gomide","doi":"10.1109/EAIS.2016.7502372","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502372","url":null,"abstract":"The correct pricing of financial derivatives plays a key role in risk management and in hedge operations. Besides the Black and Scholes closed-form formula simplicity and good results for pricing European options, several of the assumptions used in the method may be unrealistic and influence the results significantly. In order to overcome this limitation, this paper suggests an evolving possibilistic fuzzy modeling (ePFM) approach for European equity options pricing. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling. ePFM employs memberships and typicalities to recursively cluster data, and uses participatory learning to adapt the model structure as a stream data is input. The model does not require any assumptions about data distribution, it is an effective robust method to handle noisy data and outliers in option price dynamics modeling, and it is also capable to access volatility clustering due to its clustering-based nature. Computational experiments consider the pricing of European equity options (calls and puts) on preference shares of Petrobras (PETR4), one of the most liquidity options traded in the Brazilian derivatives market. The results show that ePFM is a potential candidate for equity options pricing, with comparable or better performance than the Black and Scholes method and alternative evolving fuzzy approaches.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"66 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":"125338324","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

Adaptive intelligent systems applied to two-wheeled robot 自适应智能系统在两轮机器人中的应用

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502505

Sender Rocha dos Santos, Jorge Luís Machado do Amaral, Jose F. M. Amaral

引用次数: 3

An evolving algorithm based on unobservable components neuro-fuzzy model for time series forecasting 基于不可观测分量神经模糊模型的时间序列预测进化算法

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502502

Selmo Eduardo Rodrigues, G. Serra

{"title":"An evolving algorithm based on unobservable components neuro-fuzzy model for time series forecasting","authors":"Selmo Eduardo Rodrigues, G. Serra","doi":"10.1109/EAIS.2016.7502502","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502502","url":null,"abstract":"The forecasting and characterization of time series are very useful for experts to take appropriate decisions, to plan actions and to understand the time series patterns. However, there are a small number of methods that consider both objectives at the same time. In this paper, an algorithm for nonstationary and seasonal time series forecasting, with an evolving neuro-fuzzy Takagi-Sugeno (NF-TS) structure, is proposed. For this algorithm, the NF-TS inputs are unobservable patterns extracted from the time series by a decomposition technique. As experiment, a real seasonal time series was used to compare the forecasting performance of these proposed algorithm with an other similar NF-TS, whose inputs were formed by autoregressive data from the same time series. When there is available observations from time series, the NF-TS evolves its structure and adapt its parameters. If the data is not available, the proposed methodology needs to forecast the next value. In order to extract the unobservable components from time series, the Holt-Winters method optimized by Particle Swarm Optimization (PSO) approach was considered in this experiment.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"7 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":"126234265","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

A stochastic adaptive genetic algorithm for solving unconstrained multimodal numerical problems 求解无约束多模态数值问题的随机自适应遗传算法

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502503

Egidio Carvalho, O. Cortes, João Pedro Augusto Costa, A. Rau-Chaplin

引用次数: 4

Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream 高频交易金融数据流的在线演化模糊规则预测模型

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502509

Xiaowei Gu, P. Angelov, A. Ali, W. Gruver, G. Gaydadjiev

引用次数: 15