2006 International Symposium on Evolving Fuzzy Systems最新文献_第2页

A Fuzzy Clustering Technique for Medical Image Segmentation 医学图像分割中的模糊聚类技术

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251140

M. Tabakov

引用次数: 37

Roof Shape Generation Method for Buildings Using KANSEI Evaluation Rules 基于感性评价规则的建筑物屋顶形状生成方法

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251172

K. Tsutsumi, Y. Omori, K. Sasaki

引用次数: 1

Fuzzy Models for the Study of Hydro Power Plant Dynamics 水电站动力学研究的模糊模型

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251131

N. Kishor, S. Singh, A. S. Raghuvanshi, P. Sharma

引用次数: 5

An Approach to Real-time Color-based Object Tracking 一种基于颜色的实时目标跟踪方法

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251169

M. Asif, P. Angelov, H. Ahmed

{"title":"An Approach to Real-time Color-based Object Tracking","authors":"M. Asif, P. Angelov, H. Ahmed","doi":"10.1109/ISEFS.2006.251169","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251169","url":null,"abstract":"Object tracking is of great interest in different areas of industry, security and defense. Tracking moving objects based on color information is more robust than systems utilizing motion cues. In order to maintain the lock on the object as the surrounding conditions vary, the color model needs to be adapted in real-time. In this paper an on-line learning method for the color model is implemented using fuzzy adaptive resonance theory (ART). Fuzzy ART is a type of neural network that is trained based on competitive learning principle. The color model of the target region is regularly updated based on the vigilance criteria (which is a threshold) applied to the pixel color information. The target location in the next frame is predicted using evolving extended Takagi-Sugeno (exTS) model to improve the tracking performance. The results of applying exTS for prediction of the position of the moving target were compared with the usually used solution based on Kalman filter. The experiments with real footage demonstrate over a variety of scenarios the superiority of the exTS as a predictor comparing to the Kalman filter. Further investigation concentrates on using evolving clustering for realizing computationally efficient simultaneous tracking of different segments in the object","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"87 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127997325","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: 22

Spatial Interpolation of Traffic Data by Genetic Fuzzy System 基于遗传模糊系统的交通数据空间插值

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251176

D. Ichiba, K. Hara, H. Kanoh

引用次数: 7

An Adaptive Fuzzy Model for Personalization with Evolvable User Profiles 具有可演化用户特征的自适应模糊个性化模型

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251160

G. Magoulas, D. Dimakopoulos

引用次数: 6

Process Safety Enhancements for Data-Driven Evolving Fuzzy Models 数据驱动演化模糊模型的过程安全性增强

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251173

E. Lughofer

{"title":"Process Safety Enhancements for Data-Driven Evolving Fuzzy Models","authors":"E. Lughofer","doi":"10.1109/ISEFS.2006.251173","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251173","url":null,"abstract":"In this paper several improvements towards a safer processing of incremental learning techniques for fuzzy models are demonstrated. The first group of improvements include stability issues for making the evolving scheme more robust against faults, steady state situations and extrapolation occurrence. In the case of steady states or constant system behaviors a concept of overcoming the so-called 'unlearning' effect is proposed by which the forgetting of previously learned relationships can be prevented. A discussion on the convergence of the incremental learning scheme to the optimum in the least squares sense is included as well. The concepts regarding fault omittance are demonstrated, as usually faults in the training data lead to problems in learning underlying dependencies. An improvement of extrapolation behavior in the case of fuzzy models when using fuzzy sets with infinite support is also highlighted. The second group of improvements deals with interpretability and quality aspects of the models obtained during the evolving process. An online strategy for obtaining better interpretable models is presented. This strategy is feasible for online monitoring tasks, as it can be applied after each incremental learning step, that is without using prior data. Interpretability is important, whenever the model itself or the model decisions should be linguistically understandable. The quality aspects include an online calculation of local error bars for Takagi-Sugeno fuzzy models, which can be seen as a kind of confidence intervals. In this sense, the error bars can be exploited in order to give feedback to the operator, regarding fuzzy model reliability and prediction quality. Evaluation results based on experimental results are included, showing clearly the impact on the improvement of robustness of the learning procedure","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123331522","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: 12

Evolving Intelligent Systems: Methods, Learning, & Applications 进化的智能系统:方法、学习和应用

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251185

N. Kasabov, Dimitar Filev

{"title":"Evolving Intelligent Systems: Methods, Learning, & Applications","authors":"N. Kasabov, Dimitar Filev","doi":"10.1109/ISEFS.2006.251185","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251185","url":null,"abstract":"The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize human knowledge that still separates humans from machines. Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the progress in Genetic Algorithms, Evolutionary Computing, and Genetic Programming. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted attention. These systems called `evolving' came as a result of the research on practical intelligent systems and on-line learning algorithms that are capable of extracting knowledge from data and performing a higher level adaptation of model structure as well as model parameters. Evolving systems can also be considered an extension of the multi-model concept known from the control theory, and of the on-line identification of fuzzy rule-based models. They can also be regarded as an extension of the methods for on-line learning neural networks with flexible structure that can grow and shrink.","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130514038","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: 61

Robust Recursive Fuzzy Clustering-Based Segmentation of Biological Time Series 基于鲁棒递归模糊聚类的生物时间序列分割

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251141

Y. Gorshkov, I. Kokshenev, Y. Bodyanskiy, V. Kolodyazhniy, O. Shylo

引用次数: 11

Evolutionary Design of Fuzzy Controllers Based on Messy Coding for a Miniature Mobile Robot 基于混沌编码的微型移动机器人模糊控制器进化设计

2006 International Symposium on Evolving Fuzzy Systems Pub Date : 2006-11-30 DOI: 10.1109/ISEFS.2006.251164

Rodney A. Gomez, Katherine Lugo, Eric Vallejo

引用次数: 1