2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献_第7页

Genetic programming based Choquet integral for multi-source fusion 基于遗传规划的Choquet积分多源融合

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015481

Ryan E. Smith, Derek T. Anderson, Alina Zare, J. Ball, B. Smock, J. Fairley, S. Howington

{"title":"Genetic programming based Choquet integral for multi-source fusion","authors":"Ryan E. Smith, Derek T. Anderson, Alina Zare, J. Ball, B. Smock, J. Fairley, S. Howington","doi":"10.1109/FUZZ-IEEE.2017.8015481","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015481","url":null,"abstract":"While the Choquet integral (Chi) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single Chi. Namely, we are interested in tasks where different subsets of inputs require different Chls. Herein, a genetic program (GF) is used to extend the Chi, referred to as GpChI hereafter, specifically in terms of compositions of Chls and/or arithmetic combinations of Chls. An algorithm is put forth to leam the different GP Chls via genetic algorithm (GA) optimization. Synthetic experiments demonstrate GpChI in a controlled fashion, i.e., we know the answer and can compare what is learned to the truth. Real-world experiments are also provided for the mult-sensor fusion of electromagnetic induction (EMI) and ground penetrating radar (GPR) for explosive hazard detection. Our mutli-sensor fusion experiments show that there is utility in changing aggregation strategy per different subsets of inputs (sensors or algorithms) and fusing those results.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"9 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":"124939277","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: 7

A lab-scale heliostat positioning control using fuzzy logic based stepper motor drive with micro step and multi-frequency mode 基于模糊逻辑的微步多频步进电机驱动定日镜定位控制

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015479

N. Jirasuwankul, C. Manop

引用次数: 9

Towards attribute reduction in object-oriented concept lattices 面向对象概念格中的属性约简

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015694

J. Medina, Eloísa Ramírez-Poussa

引用次数: 1

Criticality analysis of purchased materials based on fuzzy signatures 基于模糊特征的采购物资临界性分析

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015649

Gábor Farkas, P. Földesi, L. Kóczy

引用次数: 1

A scalable evolutionary linguistic fuzzy system with adaptive defuzzification in big data 大数据中具有自适应去模糊化的可扩展进化语言模糊系统

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015753

Antonio A. Márquez, F. A. Márquez, A. Peregrín

引用次数: 9

Output regulation of large-scale T-S fuzzy-model-based decentralized control systems with unknown interconnection terms 具有未知互联项的基于T-S模糊模型的大型分散控制系统输出调节

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015712

Y. Jang, H. Kim, Y. Joo, Jin Bae Park

引用次数: 2

Design of fuzzy logic controllers for decentralized voltage regulation in grid connected photovoltaic systems 并网光伏系统分散电压调节模糊控制器设计

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015395

Hafsa Qamar, Haleema Qamar, A. Vaccaro

引用次数: 1

Bipolar fuzzy relation equations based on the product T-norm 基于乘积t范数的双极模糊关系方程

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015691

M. E. Cornejo, David Lobo, J. Medina

引用次数: 8

A hesitant fuzzy linguistic model for emergency decision making based on fuzzy TODIM method 基于模糊TODIM方法的应急决策犹豫模糊语言模型

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015550

Liang Wang, Yingming Wang, Rosa M. Rodríguez, L. Martínez

{"title":"A hesitant fuzzy linguistic model for emergency decision making based on fuzzy TODIM method","authors":"Liang Wang, Yingming Wang, Rosa M. Rodríguez, L. Martínez","doi":"10.1109/FUZZ-IEEE.2017.8015550","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015550","url":null,"abstract":"The importance of emergency decision making (EDM) has grown up in recent years because of the frequent occurrence of multiple emergency events (EEs) that have caused important social and economic losses. EDM plays a relevant role when it is necessary to mitigate property and lives losses and reducing the negative impacts on the social and environmental development. Real-world EDM problems are usually characterized by complexity, hard time constraints, lack of information and the impact of the psychological behaviors which makes it very challenging task for the decision maker. This characterization shows the need of dealing with different types of uncertainty and the managing of behaviors to face these problems. This contribution proposes a new emergency decision model that first, uses fuzzy linguistic information to model the subjective information elicited by the decision maker under uncertainty and also the modelling of his/her hesitancy for assessing his/her judgements by using hesitant fuzzy linguistic term sets. Second it integrates the decision maker's psychological behavior by using the prospect theory in a fuzzy based environment. Finally, an example of application of the decision model is carried out to show its validity and applicability.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"02 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":"129681112","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: 7

Multiobjective fuzzy genetics-based machine learning based on MOEA/D with its modifications 基于MOEA/D及其修正的多目标模糊遗传机器学习

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2017-07-01 DOI: 10.1109/FUZZ-IEEE.2017.8015749

Y. Nojima, Koki Arahari, Shuji Takemura, H. Ishibuchi

{"title":"Multiobjective fuzzy genetics-based machine learning based on MOEA/D with its modifications","authors":"Y. Nojima, Koki Arahari, Shuji Takemura, H. Ishibuchi","doi":"10.1109/FUZZ-IEEE.2017.8015749","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015749","url":null,"abstract":"Various evolutionary multiobjective optimization (EMO) algorithms have been used in the field of evolutionary fuzzy systems (EFS), because EMO algorithms can easily handle multiple objective functions such as the accuracy maximization and complexity minimization for fuzzy system design. Most EMO algorithms used in EFS are Pareto dominance-based algorithms such as NSGA-II, SPEA2, and PAES. There are a few studies where other types of EMO algorithms are used in EFS. In this paper, we apply a multiobjective evolutionary algorithm based on decomposition called MOEA/D to EFS for fuzzy classifier design. MOEA/D is one of the most well-known decomposition-based EMO algorithms. The key idea is to divide a multiobjective optimization problem into a number of single-objective problems using a set of uniformly distributed weight vectors in a scalarizing function. We propose a new scalarizing function called an accuracy-oriented function (AOF) which is specialized for classifier design. We examine the effects of using AOF in MOEA/D on the search ability of our multiobjective fuzzy genetics-based machine learning (GBML). We also examine the synergy effect of MOEA/D with AOF and parallel distributed implementation of fuzzy GBML on the generalization ability.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"35 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":"124173060","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