2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第6页

Bilinear equations and fuzzy image comparison 双线性方程与模糊图像比较

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015397

F. D. Martino, S. Sessa

引用次数: 0

Image change detection by possibility distribution dissemblance 基于可能性分布伪装的图像变化检测

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015641

Charles Lesniewska-Choquet, A. Atto, G. Mauris, G. Mercier

引用次数: 2

Spot welding monitoring system based on fuzzy classification and deep learning 基于模糊分类和深度学习的点焊监测系统

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015618

Ander Muniategui, B. Heriz, Luka Eciolaza, Mikel Ayuso, A. Iturrioz, I. Quintana, P. Álvarez

{"title":"Spot welding monitoring system based on fuzzy classification and deep learning","authors":"Ander Muniategui, B. Heriz, Luka Eciolaza, Mikel Ayuso, A. Iturrioz, I. Quintana, P. Álvarez","doi":"10.1109/FUZZ-IEEE.2017.8015618","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015618","url":null,"abstract":"This work is a continuation of our previous work on the development of a monitoring system of a Spot Welding production line. Here we use the process information and photographs of more than 150,000 parts to improve the predictions of the previously developed fuzzy algorithm to predict the degradation state of the electrode. And, we present an alternative method based on deep-learning that aims at substituting the image analysis software developed by us to extract values associated with the quality level of the welded parts from photographs. The deep-learning algorithm learned here is applied to compress original photographs to a 15×15 pixels size image using an encoding / decoding model. Obtained compressed images are then used to predict quality parameters from a fuzzy rule-based classification algorithm. The results are promising and show that compressed images keep the relevant information from the original image that serve to directly determine the degree of the degradation of the electrode without requiring the use of previously developed image analysis software.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129919558","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: 13

NSGA-II based multi-objective pollution routing problem with higher order uncertainty 基于NSGA-II的高阶不确定性多目标污染路径问题

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015668

Amit K. Shukla, Rahul Nath, Pranab K. Muhuri

引用次数: 16

Chattering-free blood glucose level control based on ANFIS 基于ANFIS的无颤振血糖控制

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-09 DOI: 10.1109/FUZZ-IEEE.2017.8015734

R. Ramezanzadeh, Seyed Mahdi Hadad Baygi, Javad Farzaneh, A. Karsaz

引用次数: 2

A social choice approach to graded soft sets 分级软集的社会选择方法

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015428

Fatia Fatimah, D. Rosadi, R. B. F. Hakim, J. Alcantud

引用次数: 12

SHCoClust, a scalable similarity-based hierarchical co-clustering method and its application to textual collections SHCoClust，一种可扩展的基于相似性的分层共聚类方法及其在文本集合中的应用

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015720

Xinyu Wang, Julien Ah-Pine, J. Darmont

{"title":"SHCoClust, a scalable similarity-based hierarchical co-clustering method and its application to textual collections","authors":"Xinyu Wang, Julien Ah-Pine, J. Darmont","doi":"10.1109/FUZZ-IEEE.2017.8015720","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015720","url":null,"abstract":"In comparison with flat clustering methods, such as K-means, hierarchical clustering and co-clustering methods are more advantageous, for the reason that hierarchical clustering is capable to reveal the internal connections of clusters, and co-clustering can yield clusters of data instances and features. Interested in organizing co-clusters in hierarchy and in discovering cluster hierarchies inside co-clusters, in this paper, we propose SHCoClust, a scalable similarity-based hierarchical co-clustering method. Except possessing the above-mentioned advantages in unison, SHCoClust is able to employ kernel functions, thanks to its utilization of inner product. Furthermore, having all similarities between 0 and 1, the input of SHCoClust can be sparsified by threshold values, so that less memory and less time are required for storage and for computation. This grants SHCoClust scalability, i.e, the ability to process relatively large datasets with reduced and limited computing resources. Our experiments demonstrate that SHCoClust significantly outperforms the conventional hierarchical clustering methods. In addition, with sparsifying the input similarity matrices obtained by linear kernel and by Gaussian kernel, SHCoClust is capable to guarantee the clustering quality, even when its input being largely sparsified. Consequently, up to 86% time gain and on average 75% memory gain are achieved.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121809505","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

Developing deep fuzzy network with Takagi Sugeno fuzzy inference system 利用Takagi Sugeno模糊推理系统开发深度模糊网络

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015718

Shreedharkumar D. Rajurkar, N. Verma

{"title":"Developing deep fuzzy network with Takagi Sugeno fuzzy inference system","authors":"Shreedharkumar D. Rajurkar, N. Verma","doi":"10.1109/FUZZ-IEEE.2017.8015718","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015718","url":null,"abstract":"The state-of-art algorithms in computational intelligence have become better than human intelligence in some of pattern recognition areas. Most of these state-of-art algorithms have been developed from the concept of multi-layered artificial neural networks. Large amount of numerical and linguistic rule data has been created in recent years. Fuzzy sets are useful in modeling uncertainty due to vagueness, ambiguity and imprecision. Fuzzy inference systems incorporate linguistic rules intelligible to human beings. Many attempts have been made to combine assets of fuzzy sets, fuzzy inference systems and artificial neural networks. Use of a single fuzzy inference system limits the performance. In this paper, we propose a generic architecture of multi-layered network developed from Takagi Sugeno fuzzy inference systems as basic units. This generic architecture is called “Takagi Sugeno Deep Fuzzy Network”. Multiple distinct fuzzy inference structures can be identified using proposed architecture. A general three layered TS deep fuzzy network is explained in detail in this paper. The generic algorithm for identification of all network parameters of three layered deep fuzzy network using error backpropagation is presented in the paper. The proposed architecture as well as its identification procedure are validated using two experimental case studies. The performance of proposed architecture is evaluated in normal, imprecise and vague situations and it is compared with performance of artificial neural network with same architecture. The results illustrate that the proposed architecture eclipses over three layered feedforward artificial neural network in all situations.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124243376","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: 34

Towards a fuzzy volatility index for the Italian market 意大利市场的波动指数趋于模糊

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015446

S. Muzzioli, Luca Gambarelli, B. Baets

引用次数: 4

Fuzzy decision tree and fuzzy gradual decision tree: Application to job satisfaction 模糊决策树与模糊渐进决策树:在工作满意度研究中的应用

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015740

C. Marsala, M. Rifqi

引用次数: 1