2017 Evolving and Adaptive Intelligent Systems (EAIS)最新文献

Robust Evolving Cloud-based Controller (RECCo) 基于云的鲁棒进化控制器(RECCo)

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-06-02 DOI: 10.1109/EAIS.2017.7954835

G. Andonovski, P. Angelov, S. Blažič, I. Škrjanc

引用次数: 4

Autonomous anomaly detection 自主异常检测

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-31 DOI: 10.1109/EAIS.2017.7954831

Xiaowei Gu, P. Angelov

引用次数: 16

Autonomous learning multi-model classifier of 0-Order (ALMMo-0) 0阶自主学习多模型分类器(ALMMo-0)

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-31 DOI: 10.1109/EAIS.2017.7954832

P. Angelov, Xiaowei Gu

引用次数: 29

A sensory-neural network for medical diagnosis 用于医学诊断的感觉神经网络

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954819

Mihael Sok, Eva Svegl, I. Grabec

引用次数: 3

Incremental rule splitting in generalized evolving fuzzy regression models 广义演化模糊回归模型中的增量规则分裂

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954836

E. Lughofer, Mahardhika Pratama, I. Škrjanc

{"title":"Incremental rule splitting in generalized evolving fuzzy regression models","authors":"E. Lughofer, Mahardhika Pratama, I. Škrjanc","doi":"10.1109/EAIS.2017.7954836","DOIUrl":"https://doi.org/10.1109/EAIS.2017.7954836","url":null,"abstract":"We propose an incremental rule splitting concept for generalized fuzzy rules in evolving fuzzy regression models in order to properly react on gradual drifts and to compensate inappropriate settings of rule evolution parameters; both occurrences may lead to oversized rules with untypically large local errors, which also usually affects the global model error. The generalized rules are directly defined in the multi-dimensional feature space through a kernel function, and thus allowing any rotated orientation of their shapes. Our splitting condition is based 1.) on the local error of rules measured in terms of a weighted contribution to the whole model error and 2.) on the size of the rules measured in terms of its volume. Thereby, we use the concept of statistical process control for automatic thresholding, in order to omit two extra parameters. The splitting technique relies on the eigendecompisition of the rule covariance matrix by adequately manipulating the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Thus, splitting is performed along the main principal component direction of a rule. The splitting concepts are integrated in the generalized smart evolving learning engine (Gen-Smart-EFS) and successfully tested on two real-world application scenarios, engine test benches and rolling mills, the latter including a real-occurring gradual drift (whose position in the data is known). Results show clearly improved error trend lines over time when splitting is applied: reduction of the error by about one third (rolling mills) and one half (engine test benches). In case of rolling mills, three rule splits right after the gradual drift starts were essential for this significant improvement.","PeriodicalId":286312,"journal":{"name":"2017 Evolving and Adaptive Intelligent Systems (EAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128435647","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

Modeling and simulation of a small wind turbine system based on PMSG generator 基于PMSG发电机的小型风力发电系统建模与仿真

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954833

Rim Ben Ali, H. Schulte, A. Mami

引用次数: 15

On-line identification with regularised Evolving Gaussian process 正则化演化高斯过程在线辨识

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954820

M. Stepancic, J. Kocijan

引用次数: 2

An immune evolution mechanism for the study of stress factors in supervised and controlled systems 在监督和控制系统中研究应激因子的免疫进化机制

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954823

M. Pătrașcu, Adrian Patrascu, I. Beres

{"title":"An immune evolution mechanism for the study of stress factors in supervised and controlled systems","authors":"M. Pătrașcu, Adrian Patrascu, I. Beres","doi":"10.1109/EAIS.2017.7954823","DOIUrl":"https://doi.org/10.1109/EAIS.2017.7954823","url":null,"abstract":"Reinterpretation and adaptation of knowledge from technical sciences into the field of sports science is at the forefront of advancing performance. Evolution-based systems open the possibility of evaluating different stress factors that appear in the systematization of training programs. Our aim was to demonstrate that it is possible to use evolution mechanisms to model the real life influence of stress factors during performance training of athletes, a concept that can be generalized to other supervised or controlled systems. We have recorded data from one former basketball player to be used in the development of a simulation model based on immune genetic algorithms. We developed an immunization scheme that is parameterized for simulating different training outcomes based on type of athlete. Results confirm one of these cases is possibly the most similar to real life situations. Thus, we obtained an evolution model that aligns with the generative experiment as proof of concept for the evaluation of performance under stress. In particular, for sports science, we may have found a way to analyze training programs before their execution and to spot weaknesses in them.","PeriodicalId":286312,"journal":{"name":"2017 Evolving and Adaptive Intelligent Systems (EAIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123227197","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

An implementation of an evolving fuzzy controller 一种进化模糊控制器的实现

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954830

S. Blažič, Andrej Zdešar

引用次数: 1

Evolving participatory learning fuzzy modeling for financial interval time series forecasting 金融区间时间序列预测的演化参与式学习模糊模型

2017 Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2017-05-01 DOI: 10.1109/EAIS.2017.7954826

Leandro Maciel, R. Vieira, Alisson Porto, F. Gomide, R. Ballini

{"title":"Evolving participatory learning fuzzy modeling for financial interval time series forecasting","authors":"Leandro Maciel, R. Vieira, Alisson Porto, F. Gomide, R. Ballini","doi":"10.1109/EAIS.2017.7954826","DOIUrl":"https://doi.org/10.1109/EAIS.2017.7954826","url":null,"abstract":"Financial interval time series (ITS) describe the evolution of the maximum and minimum prices of an asset throughout time. These price ranges are related to the concept of volatility. Hence, their accurate forecasts play a key role in risk management, derivatives pricing and asset allocation, as well as supplements the information extracted by the time series of the closing price values. This paper addresses evolving fuzzy systems and financial ITS forecasting considering as the empirical application the main index of the Brazilian stock market, the IBOVESPA. An evolving participatory learning fuzzy model, named ePL-KRLS, is proposed. The model extends traditional ePL approach by considering Kernel functions to the identification of rule consequents parameters as well as a metaheuristic algorithm to automatically set model control parameters. One step ahead interval forecasts is compared against linear and nonlinear time series benchmark methods and with the state of the art evolving fuzzy models in terms of traditional accuracy metrics and quality measures designed for ITS. The results provide evidence for the predictability of of IBOVESPA ITS and significant forecast contribution of ePL-KRLS.","PeriodicalId":286312,"journal":{"name":"2017 Evolving and Adaptive Intelligent Systems (EAIS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126516210","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: 9