2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第2页

Interval type-2 fuzzy co-clustering algorithm 区间2型模糊共聚类算法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337960

Van Nha Pham, L. Ngo

引用次数: 1

Online SOC estimation of Li-FePO4 batteries through an observer of the system state with minimal nonspecificity 利用非特异性最小的系统状态观测器在线估计fepo4锂电池的SOC

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337901

L. Sánchez, Inés Couso, C. B. Viejo

引用次数: 0

Discovering the value of a patent licensing opportunity with a fuzzy binomial model 用模糊二项模型发现专利许可机会的价值

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337955

Xiaolu Wang, C. Carlsson

引用次数: 1

A novel Multiple Attribute Group Decision Making methodology based on Intuitionistic Fuzzy TOPSIS 基于直觉模糊TOPSIS的多属性群决策方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338119

Beyzanur Cayir Ervural, S. C. Öner, Veysel Çoban, C. Kahraman

引用次数: 8

Reducing the complexity in genetic learning of accurate regression TSK rule-based systems 降低基于规则的精确回归TSK系统遗传学习的复杂性

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337930

Ismael Rodríguez-Fdez, M. Mucientes, Alberto Bugarín-Diz

{"title":"Reducing the complexity in genetic learning of accurate regression TSK rule-based systems","authors":"Ismael Rodríguez-Fdez, M. Mucientes, Alberto Bugarín-Diz","doi":"10.1109/FUZZ-IEEE.2015.7337930","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337930","url":null,"abstract":"In many real problems the regression models have to be accurate but, also, interpretable in order to provide qualitative understanding of the system. In this realm, the use of fuzzy rule base systems, particularly TSK, is widely extended. TSK rules combine the interpretability and expressiveness of rules with the ability of fuzzy logic for representing uncertainty, and the precision of the polynomials in the consequents. In this paper we present a new genetic fuzzy system to automatically learn accurate and simple linguistic TSK fuzzy rule bases that accurately model regression problems. In order to reduce the complexity of the learned models while keeping a high accuracy, we propose a Genetic Fuzzy System which consists of three stages: instance selection, multi-granularity fuzzy discretization of the input variables, and the evolutionary learning of the rule base using Elastic Net regularization. This proposal was validated using 28 real-world datasets and compared with three state of the art genetic fuzzy systems. Results show that our approach obtains the simplest models while achieving a similar accuracy to the best approximative models.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255190","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: 5

Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT) 基于遗传模糊度量技术(GFT)的学生建议决策预测学生未来GPA

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337925

I. Kouatli

{"title":"Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT)","authors":"I. Kouatli","doi":"10.1109/FUZZ-IEEE.2015.7337925","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337925","url":null,"abstract":"Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. Combining these techniques provides an enhanced capability of any decision support systems (DSS. This paper discusses a modular approach toward implementing Genetic Fuzzy system termed as “Genetic Fuzzimetric Technique” (GFT). The technique utilizes input importance factor to combine the modular structure into final decision process. The objective of this combination provides the ability of the system to interact and “take decision” in an environment in the same manner as the human decision maker would do. This proposed system is ideal in cases where mathematical modeling either does not exist or insufficient for appropriate decision making under uncertainty. Most of real life decision making processes are of that type of uncertainty. One such problem is to decide on the predicted GPA level for students during the admission process to the university. This is mainly dependent on High School (HS) performance, Sophomore Exam (SE) results and English exam (EEE) performance. Looking at the historical data of students, fuzzy logic can be used to develop rules based on these data. Genetic Algorithm would be used to optimize the performance of the system.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888220","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

Hybrid approach in recognition of visual covert selective spatial attention based on MEG signals 基于MEG信号的视觉隐蔽选择性空间注意识别的混合方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337958

S. A. Hosseini, M. Akbarzadeh-Totonchi, M. Naghibi-Sistani

{"title":"Hybrid approach in recognition of visual covert selective spatial attention based on MEG signals","authors":"S. A. Hosseini, M. Akbarzadeh-Totonchi, M. Naghibi-Sistani","doi":"10.1109/FUZZ-IEEE.2015.7337958","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337958","url":null,"abstract":"This paper proposes a reliable and efficient method for recognition in two different orientations (either left or right) by Magnetoencephalograph (MEG) signals. The brain activities are measured using different approaches with different spatial and temporal resolutions. The MEG signals are usually used for brain-computer interface (BCI) applications due to high temporal resolution. The MEG signals were recorded from different brain regions of four different human subjects during visual covert selective spatial attention task. The hybrid method proposes pre-processing; feature extraction by Hurst exponent, Morlet wavelet coefficients, and Petrosian fractal dimension; normalization; feature selection by p-value; and classification by support vector machine (SVM) and fuzzy support vector machine (FSVM). The results show that the proposed method can predict the location of the attended stimulus with a high accuracy of 91.62% and 92.28% for two different orientations with SVM and FSVM, respectively. Finally, these methods can be useful for BCI applications based on visual covert selective spatial attention.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455145","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

Fuzzy Morphological Extreme Learning Machines to detect and classify masses in mammograms 模糊形态学极限学习机检测和分类乳房x光片肿块

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337975

Washington W. Azevedo, Sidney M. L. Lima, Isabella M. M. Fernandes, A. D. D. Rocha, F. Cordeiro, Abel G. da Silva Filho, W. Santos

{"title":"Fuzzy Morphological Extreme Learning Machines to detect and classify masses in mammograms","authors":"Washington W. Azevedo, Sidney M. L. Lima, Isabella M. M. Fernandes, A. D. D. Rocha, F. Cordeiro, Abel G. da Silva Filho, W. Santos","doi":"10.1109/FUZZ-IEEE.2015.7337975","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337975","url":null,"abstract":"According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer assisted diagnosis is important to avoid misdiagnoses. In this work, we propose Fuzzy Morphological Extreme Learning Machines, with hidden layer kernel based on nonlinear morphological operators of erosion and dilation. The proposed methods were evaluated using 2.796 images from IRMA database, considering fat, fibroid, dense and extremely dense tissues. Zernike Moments and Haralick texture features are used as image descriptors. The proposed model classifies masses as benign, malignant or normal. Results shows comparison between Extreme Learning Machines using Sigmoid and Fuzzy Morphological Kernels, evaluated using classification rate and Kappa index. When using fuzzy morphological kernels, classification rate and Kappa value increases for most of cases analyzed.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199569","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: 27

Computational intelligence in forecasting - the results of the time series forecasting competition 预测中的计算智能——时间序列预测竞赛的结果

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337986

M. Štěpnička, M. Burda

引用次数: 5

ICP based neonatal brain MRI normalization method 基于ICP的新生儿脑MRI归一化方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337920

Kento Morita, Syoji Kobashi, Yuki Wakata, K. Ando, R. Ishikura, N. Kamiura

{"title":"ICP based neonatal brain MRI normalization method","authors":"Kento Morita, Syoji Kobashi, Yuki Wakata, K. Ando, R. Ishikura, N. Kamiura","doi":"10.1109/FUZZ-IEEE.2015.7337920","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337920","url":null,"abstract":"Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116408126","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