2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第6页

Hesitant fuzzy analytic hierarchy process 犹豫模糊层次分析法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337948

Başar Öztayşi, Sezi Cevik Onar, Eda Boltürk, C. Kahraman

引用次数: 65

Combination of Sugeno fuzzy system and evidence theory for NAO robot in colors recognition Sugeno模糊系统与证据理论在NAO机器人颜色识别中的结合

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337900

T. Nguyen, R. Boukezzoula, D. Coquin, S. Perrin

引用次数: 2

An adaptive neuro-fuzzy model for the detection of meat spoilage using multispectral images 基于多光谱图像的肉类腐败检测自适应神经模糊模型

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337961

Abeer Alshejari, V. Kodogiannis, I. Petrounias

引用次数: 2

“Give me what I want” - enabling complex queries on rich multi-attribute data “给我我想要的”——支持对丰富的多属性数据进行复杂查询

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337865

J. McCulloch, Christian Wagner, K. Bachour, T. Rodden

引用次数: 2

Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic 主动粒子群优化:一种基于模糊逻辑的自整定算法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337957

Marco S. Nobile, G. Pasi, P. Cazzaniga, D. Besozzi, R. Colombo, G. Mauri

{"title":"Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic","authors":"Marco S. Nobile, G. Pasi, P. Cazzaniga, D. Besozzi, R. Colombo, G. Mauri","doi":"10.1109/FUZZ-IEEE.2015.7337957","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337957","url":null,"abstract":"Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles in Swarm Optimization (PPSO) - which exploits Fuzzy Logic to calculate the best setting for the inertia, cognitive factor and social factor. Thanks to additional heuristics, PPSO automatically determines also the best setting for the swarm size and for the particles maximum velocity. PPSO significantly differs from other versions of PSO that exploit Fuzzy Logic, since specific settings are assigned to each particle according to its history, instead of being globally defined for the whole swarm. Thus, the novelty of PPSO is that particles gain a limited autonomous and proactive intelligence, instead of being simple reactive agents. Our results show that PPSO outperforms the standard PSO, both in terms of convergence speed and average quality of solutions, remarkably without the need for any user setting.","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":"114388053","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: 28

A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas 基于遗传2型模糊逻辑的移动现场工程师工作区域优化分配方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337869

A. Starkey, H. Hagras, S. Shakya, G. Owusu

{"title":"A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas","authors":"A. Starkey, H. Hagras, S. Shakya, G. Owusu","doi":"10.1109/FUZZ-IEEE.2015.7337869","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337869","url":null,"abstract":"In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"30 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":"127192805","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: 10

Automatic seizure detection in multichannel EEG using McCIT2FIS approach 基于McCIT2FIS方法的多通道脑电图癫痫自动检测

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338085

Shirin Dora, B. Rangarajan, K. Subramanian, S. Sundaram

{"title":"Automatic seizure detection in multichannel EEG using McCIT2FIS approach","authors":"Shirin Dora, B. Rangarajan, K. Subramanian, S. Sundaram","doi":"10.1109/FUZZ-IEEE.2015.7338085","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7338085","url":null,"abstract":"In this paper, an automatic seizure detection technique using multichannel EEG is proposed based on Metacognitive Complex-valued Interval Type-2 Fuzzy Inference System (McCIT2FIS). A wavelet chaos theory based feature extraction is employed to extract the features from EEG signal as it can handle the non stationarity in data and Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation (SBMLR) based feature selection is employed to select the most discriminative features. McCIT2FIS is employed to classify the samples as either interictal or ictal EEG segment as it has been shown to be capable of handling noisy data by virtue of Interval Type-2 fuzzy sets, and is good at classification because of its ability to handle complex-valued data. Further, we have also shown that the feature selected using SBMLR can be successfully mapped back to the channels allowing us to identify the epileptogenic regions of the brain. The performance of the McCIT2FIS was also compared with the support vector machines and the results indicate that McCIT2FIS is better capable of detecting seizure based on EEG signals.","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":"127252697","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: 1

Revisiting KM algorithms: A Linear Programming approach 回顾KM算法:一种线性规划方法

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337871

T. Kumbasar

{"title":"Revisiting KM algorithms: A Linear Programming approach","authors":"T. Kumbasar","doi":"10.1109/FUZZ-IEEE.2015.7337871","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337871","url":null,"abstract":"Computing the centroid and performing Type Reduction (TR) for type-2 fuzzy sets and systems are operations that must be taken into consideration. Karnik-Mendel Algorithms (KMAs) have been usually employed to perform these operations. In KMAs, these operations are defined as nonlinear optimization problems which are solved iteratively by finding the optimal Switching Points (SPs). In this study, we will transform these operations into Linear Fractional Programming (LFP) problems and solve them with the aids of the well-developed Linear Programming (LP) theory. It will be shown that there exists a direct relationship between the SPs of the KMAs and the solution vectors of the defined LFP problems. Thus, the meaning of the SPs will be revealed in the framework of LFP theory and the KMA will be connected a LFP method. We will then present two novel LP based TR methods which only use and employ basic built-in LP functions. Thus, these LP based TR methods will be very helpful in employing type-2 fuzzy sets and systems in different programming languages. Moreover, by taking account the connection of LFP to KMAs, a computationally efficient LP based TR method will be proposed. It will be proven that this LP based TR method can be seen as a kind of variation of the KMA (or vice versa). Simulation results have been presented to show the superiority of the LP based TR method in comparison to the KMA and Enhanced KMA.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"46 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":"124834853","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: 4

Fuzzy Social Force Model 模糊社会力模型

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7338057

Altieres Del Sent, M. Roisenberg

引用次数: 2

Fuzzy rule classifiers for multi-label classification 多标签模糊规则分类器

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2015-11-30 DOI: 10.1109/FUZZ-IEEE.2015.7337815

R. Prati

引用次数: 12