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[1992 Proceedings] IEEE International Conference on Fuzzy Systems最新文献

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Fuzzy second-generation expert system design for IE/OR/MS IE/OR/MS模糊第二代专家系统设计
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258759
I. Turksen
{"title":"Fuzzy second-generation expert system design for IE/OR/MS","authors":"I. Turksen","doi":"10.1109/FUZZY.1992.258759","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258759","url":null,"abstract":"Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106067","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: 8
Learning fuzzy logic control: an indirect control approach 学习模糊逻辑控制:一种间接控制方法
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258632
B.H. Wang, G. Vachtsevanos
{"title":"Learning fuzzy logic control: an indirect control approach","authors":"B.H. Wang, G. Vachtsevanos","doi":"10.1109/FUZZY.1992.258632","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258632","url":null,"abstract":"A systematic methodology for the design of a learning fuzzy logic control system is presented. The basic design idea is an indirect control approach where selection of control parameters relies on the estimates of process parameters. The control law consists of three components: an online fuzzy identifier, a desired transition model, and a fuzzy controller. The fuzzy version of the signal Hebbian learning law is introduced for adaptively identifying the process relation of the unknown plant. The desired transition model is constructed so that the control designer's goal can be achieved. A computationally efficient way to construct the transition model is provided via a forward-in-time method based on the concept of truncated policy space. Clear trade-offs between control performance and computational complexity are obtained.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114937520","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: 14
An adaptive fuzzy system for control and clustering of arbitrary data patterns 一种用于任意数据模式控制和聚类的自适应模糊系统
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258642
S. C. Newton, S. Mitra
{"title":"An adaptive fuzzy system for control and clustering of arbitrary data patterns","authors":"S. C. Newton, S. Mitra","doi":"10.1109/FUZZY.1992.258642","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258642","url":null,"abstract":"A modular, unsupervised neural network architecture is described. It can be used for data clustering and classification. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns online in a stable and efficient manner. The system consists of a fuzzy k-means learning rule embedded within a control structure similar to that found in the adaptive resonance theory (ART-1) network. AFLC adaptively clusters analog inputs into classes without prior knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two-stage process; a simple competitive stage and a euclidean metric comparison stage. The AFLC algorithm and its operating characteristics are described. The algorithm is compared to an adaptive Bayesian classifier for some real data.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132602688","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: 8
Design of electric power system stabilizer based on fuzzy control theory 基于模糊控制理论的电力系统稳定器设计
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258788
A. Ishigame, T. Ueda, S. Kawamoto, T. Taniguchi, M. Kikuta
{"title":"Design of electric power system stabilizer based on fuzzy control theory","authors":"A. Ishigame, T. Ueda, S. Kawamoto, T. Taniguchi, M. Kikuta","doi":"10.1109/FUZZY.1992.258788","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258788","url":null,"abstract":"The authors present a method of constructing simple fuzzy control rules for controllers of electric power systems, by simplifying variables of condition parts and rules and minimizing the number of membership functions. The simplification is caused by a coordinate transformation with the rotation angle theta on the phase plane. An electric power system with controllers is modeled as a fuzzy model which is composed of a weighted average of linear systems. Based on the fuzzy model, stability analysis of the fuzzy control system is discussed. For optimal parameters setting, a quadratic performance index is considered and the quasi-Newton method is applied. The control effect was demonstrated by an application to a one-machine infinite-bus power system with an automatic voltage regulator and a governor as controllers.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132646037","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: 17
A prototype of neuro-fuzzy cooperation system 神经模糊合作系统的原型
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258595
A. Kawamura, N. Watanabe, H. Okada, K. Asakawa
{"title":"A prototype of neuro-fuzzy cooperation system","authors":"A. Kawamura, N. Watanabe, H. Okada, K. Asakawa","doi":"10.1109/FUZZY.1992.258595","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258595","url":null,"abstract":"The authors are developing a prototype of a neuro-fuzzy cooperation system that has the precision and learning ability of a neural network and is easy to understand like a fuzzy model. To help convert between neural and fuzzy systems, this system has a neural network with a structure corresponding to that of a fuzzy model. Knowledge acquired from experts was converted from a fuzzy system to a neural network. The neural network was applied to a target system and learned from data obtained during operation to enhance the accuracy of the model. Converting the neural network back into a fuzzy model helps explain the inner representation of the neural network. The model of the target system will be constructed as basic rules and will be improved step by step using a repetition of the fuzzy-neuro and neuro-fuzzy conversion.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134135506","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: 43
Architecture of a CMOS fuzzy logic controller with optimized memory organisation and operator design 具有优化存储器组织和运算符设计的CMOS模糊逻辑控制器结构
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258688
H. Eichfeld, M. Lohner, M. Muller
{"title":"Architecture of a CMOS fuzzy logic controller with optimized memory organisation and operator design","authors":"H. Eichfeld, M. Lohner, M. Muller","doi":"10.1109/FUZZY.1992.258688","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258688","url":null,"abstract":"A fuzzy logic control (FLC) unit as an on-chip part of a multi-purpose controller device is described. The architecture of the FLC is presented. The focus is on a method to implement the rule memory in a minimal memory space. A systematic analysis of the implementation of fuzzy MIN- or MAX-operators in digital CMOS circuits is included. A solution with minimal transistor count and maximal speed was found.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132134543","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: 57
Learning fuzzy information in a hybrid connectionist, symbolic model 在混合连接主义、符号模型中学习模糊信息
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258633
S. G. Romaniuk, Lawrence O. Hall
{"title":"Learning fuzzy information in a hybrid connectionist, symbolic model","authors":"S. G. Romaniuk, Lawrence O. Hall","doi":"10.1109/FUZZY.1992.258633","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258633","url":null,"abstract":"An implementation of fuzzy variables using pi-shaped membership functions is shown in a hybrid symbolic connectionist expert system tool that uses fuzzy logic to implement reasoning with uncertainty and imprecision and that can learn from imprecise data. A method of dynamically modifying the arms, or fuzzy part of the membership functions, during learning is shown. Examples illustrating the method are presented. The results indicate that the presented system is capable of learning membership functions for applications such as control or classification.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132562450","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
K-step ahead prediction in fuzzy decision space-application to prognosis 模糊决策空间中的k步预测在预测中的应用
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258806
C. Frélicot, B. Dubuisson
{"title":"K-step ahead prediction in fuzzy decision space-application to prognosis","authors":"C. Frélicot, B. Dubuisson","doi":"10.1109/FUZZY.1992.258806","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258806","url":null,"abstract":"The authors demonstrate the ability and the accuracy of a modified extended Kalman filter used as a k-step-ahead predictor to perform a predicted membership function's point in a fuzzy decision space based on fuzzy pattern recognition principles, instead of a predicted state in the feature space. Results obtained with this prediction procedure are presented. A scheme including both fuzzy decision and prediction procedures is proposed for prognosis.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"69 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944239","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
Color planning by fuzzy set theory 用模糊集理论进行色彩规划
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258672
S. Nakanishi, T. Takagi, T. Nishiyama
{"title":"Color planning by fuzzy set theory","authors":"S. Nakanishi, T. Takagi, T. Nishiyama","doi":"10.1109/FUZZY.1992.258672","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258672","url":null,"abstract":"The authors propose a system to plan a color arrangement in interior space such as rooms and offices. The system is based on three main processes: a determination process of dominant colors, a determination process of harmony colors, and an adjustment process of the resultant colors. In the first process, the dominant color of the interior space is determined from the linguistic representation of the users image for this space by fuzzy set theory. In the second process, the harmony colors for the dominant color are calculated by P. Moon and D. E. Spencer-Spencer's harmony theory, taking into account the area effect and linguistic inputs. If users cannot satisfy the resultant colors represented on a CRT display, the system can adjust the resultant colors to the user's favorite color by linguistic representation in the final process.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274115","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
Interval logic and its extension 区间逻辑及其扩展
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258727
M. Mukaidono
{"title":"Interval logic and its extension","authors":"M. Mukaidono","doi":"10.1109/FUZZY.1992.258727","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258727","url":null,"abstract":"In general, interval logic has an interval truth value (n, p), where n and p are numerical truth values of (0, 1) and a condition n<or=p has to be satisfied. The author extends interval logic such that the condition n<or=p is removed from the interval truth value, that is, the interval logic has a truth value (a,b). where a and b are any elements of (0, 1). In this extended interval logic, degrees of ambiguity and contradiction as well as degrees of true and false can be treated. By introducing two partially ordered relations on the set of truth values of interval logic, concerning truth and ambiguity, basic logic operations are defined. Some fundamental properties of an interval logic function are studied, where an interval logic function is a function represented by a logic formula consisting of these operations and variables which take interval truth values.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133959451","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
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