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

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A fuzzy programmable logic array (fuzzy PLA) 模糊可编程逻辑阵列(fuzzy PLA)
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258657
T. Yamakawa
{"title":"A fuzzy programmable logic array (fuzzy PLA)","authors":"T. Yamakawa","doi":"10.1109/FUZZY.1992.258657","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258657","url":null,"abstract":"A fuzzy programmable logic array (PLA) employing a bipolar analog technology is presented. The fuzzy PLA consists of MAX columns, MIN columns, and input/output (I/O) lines. Base fuses and emitter fuses are located in both columns. Programming is accomplished by blowing these fuses by high current pulses. Sometimes, these fuses can be replaced with thin isolation films, which are imbedded in the crossing of I/O lines and emitter lines of MAX/MIN columns. Programming of this type of fuzzy PLA is done by applying a high voltage to the selected isolation film to break down and short two crossing lines. Two applications of the fuzzy PLA are also described.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"65 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":"125747565","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: 19
Multi-attribute classification using fuzzy integral 基于模糊积分的多属性分类
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258678
M. Grabisch, M. Sugeno
{"title":"Multi-attribute classification using fuzzy integral","authors":"M. Grabisch, M. Sugeno","doi":"10.1109/FUZZY.1992.258678","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258678","url":null,"abstract":"Fuzzy set theory can provide a suitable framework for pattern classification, because of the inherent fuzziness involved in the definition of a class or a cluster. Fuzzy set theory is discussed based on a fuzzy pattern matching procedure, where partial matching values with respect to a given attribute are combined. This approach is closely related to a statistical approach to pattern classification. A new method based on a fuzzy integral and possibility theory is presented. A critical examination of the statistical approach and the supervised learning process is outlined. Experimental test results on real data are presented.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"30 21 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":"127967696","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: 114
Back-propagation fuzzy system as nonlinear dynamic system identifiers 作为非线性动态系统标识符的反向传播模糊系统
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258711
Li-Xin Wang, J. Mendel
{"title":"Back-propagation fuzzy system as nonlinear dynamic system identifiers","authors":"Li-Xin Wang, J. Mendel","doi":"10.1109/FUZZY.1992.258711","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258711","url":null,"abstract":"The authors develop a training algorithm, similar to the backpropagation algorithm for neural networks, to train fuzzy systems to match desired input-output pairs. The key ideas in developing this training algorithm are to view a fuzzy system as a three-layer feedforward network, and to use the chain rule to determine gradients of the output errors of the fuzzy system with respect to its design parameters. It is shown that this training algorithm performs an error backpropagation procedure: hence, the fuzzy system equipped with the backpropagation training algorithm is called the backpropagation fuzzy system (BP FS). An online initial parameter choosing method is proposed for the BP FS, and it is shown that it is straightforward to incorporate linguistic if-then rules into the BP FS. Two examples are presented which demonstrate (1) how the fuzzy system learns to match an unknown nonlinear mapping as training progresses and (2) that performance is improved by incorporating linguistic rules.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"36 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":"130889072","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: 493
Fuzzy control: Principles, practice and perspectives 模糊控制:原理、实践与展望
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258603
M. Sugeno
{"title":"Fuzzy control: Principles, practice and perspectives","authors":"M. Sugeno","doi":"10.1109/FUZZY.1992.258603","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258603","url":null,"abstract":"Summary form only given. It is known that fuzzy control is the most successful application of the fuzzy logic. The reasons for this success were explored in a brief survey of the history of the fuzzy control. Some practical examples of fuzzy control were reviewed. Advanced fuzzy control topics such as structured control and intelligent control were discussed. A stability analysis of a fuzzy control system was considered.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"5 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":"131090637","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: 11
Computer aided tuning and validation of fuzzy systems 模糊系统的计算机辅助整定与验证
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258731
A. Boscolo, F. Drius
{"title":"Computer aided tuning and validation of fuzzy systems","authors":"A. Boscolo, F. Drius","doi":"10.1109/FUZZY.1992.258731","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258731","url":null,"abstract":"A set of tools to support the design of fuzzy controllers is illustrated. The tools, based upon dedicated optimization algorithms, allow the tuning and the validation of the fuzzy models. A modified Monte Carlo method has been developed to define stochastic algorithms to complement the deterministic approach, because of the peculiar structure of the systems addressed. The authors show that the design tools are suitable for giving a consistent answer to the tuning needs of fuzzy control systems. During experiments the proposed algorithms have successfully lead to model optimization allowing the fulfilment of design requirements.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"33 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":"133711178","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
Reasoning by analogy with fuzzy rules 用模糊规则类推推理
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258627
L. Kóczy, K. Hirota
{"title":"Reasoning by analogy with fuzzy rules","authors":"L. Kóczy, K. Hirota","doi":"10.1109/FUZZY.1992.258627","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258627","url":null,"abstract":"In sparse systems rules do not cover the complete observation space and, in the general case, observations do not match with any of the rule antecedents, i.e. there is no direct way to compute a conclusion. A solution to this problem is presented if reasoning by analogy is applied. The basic case of reasoning by analogy is the interpolation of two rules. An extension of this method is extrapolation, another is interpolation of 2k rules. The generalization including all these methods uses an approximation covering the whole space where the complete rule system or an arbitrary subset of it can be used as the basis for the calculation of the conclusion. This generalized algorithm is sketched, and a few examples are presented.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"122 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":"124381949","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: 12
Evidence theory of normal possibility and its application 正常可能性证据理论及其应用
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258679
H. Tanaka, H. Ishibuchi
{"title":"Evidence theory of normal possibility and its application","authors":"H. Tanaka, H. Ishibuchi","doi":"10.1109/FUZZY.1992.258679","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258679","url":null,"abstract":"The authors construct a framework of evidence theory by normal possibility distributions defined as exponential functions. A possibility distribution is regarded as an evidence. A rule of combination of evidences is given with the same concept as Dempster's rule (see A. P. Dempster, 1967). Also, measures of ignorance and fuzziness of an evidence are defined by a normality factor and an area of a possibility distribution, respectively. Marginal and conditional possibilities are defined from a joint possibility distribution and it is shown that these three definitions are well matched to each other. Thus, the posterior possibility is derived from the prior possibility in the same form as Bayes's formula. Operations of fuzzy vectors defined by multidimensional possibility distributions are well formulated. Comments on an application of possibility distributions are given for discriminant analysis using fuzzy if-then rules.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"67 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":"114808107","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
Rough sets with strict and weak indiscernibility relations 具有严格和弱不可分辨关系的粗糙集
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258742
R. Słowiński
{"title":"Rough sets with strict and weak indiscernibility relations","authors":"R. Słowiński","doi":"10.1109/FUZZY.1992.258742","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258742","url":null,"abstract":"Rough sets theory is a tool for vague data analysis. The idea of the rough set consists in approximation of a set by a pair of sets called the lower and upper approximation of the set. The definition of the approximations follows from an indiscernibility relation between elements of the set, called objects. Objects are described by attributes of a qualitative or quantitative nature. In the case of quantitative attributes, the indiscernibility relation has been originally defined after partition of the real scale into a finite number of intervals. The bounds of the intervals are more or less arbitrary and may influence the result of the rough sets analysis. To capture this influence, the author introduces a strict and a weak indiscernibility relation and, according to them, the lower and upper approximations and the measures of vagueness are generalized.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"26 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":"114556211","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: 12
Fuzzy control of blood pressure during anesthesia with isoflurane 异氟醚麻醉期间血压的模糊控制
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258789
R. Meier, J. Nieuwland, S. Hacisalihzade, D. Steck, A. Zbinden
{"title":"Fuzzy control of blood pressure during anesthesia with isoflurane","authors":"R. Meier, J. Nieuwland, S. Hacisalihzade, D. Steck, A. Zbinden","doi":"10.1109/FUZZY.1992.258789","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258789","url":null,"abstract":"A fuzzy controller which controls the depth of anesthesia during surgery with isoflurane was designed and implemented on a personal computer. The mean arterial pressure (MAP) was taken as a measure for the depth of anesthesia. The design process was iterative and the reference points of the membership functions as well as the linguistic rules were determined by trial and error. The control rules made use of the error between the desired and the actual values of MAP as well as the integral of the error. The controller was tested in several surgical operations, and it was observed that the anesthetists supervising the controller never had to intervene or override it.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"24 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":"123965070","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
Adaptive fuzzy logic control 自适应模糊逻辑控制
[1992 Proceedings] IEEE International Conference on Fuzzy Systems Pub Date : 1992-03-08 DOI: 10.1109/FUZZY.1992.258648
H. Kang, G. Vachtsevanos
{"title":"Adaptive fuzzy logic control","authors":"H. Kang, G. Vachtsevanos","doi":"10.1109/FUZZY.1992.258648","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258648","url":null,"abstract":"A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. The design is based on stability and hierarchy of identification and control. The fuzzy rule-base is stored in a fuzzy hypercube and the fuzzy control action is computed via a fuzzy inference mechanism. Initial conditions for the elements of a fuzzy hypercube are obtained by an offline fuzzy clustering mechanism with large-grain uncertainty. Two fuzzy algorithms are developed: the first one is a fuzzy identification-learning algorithm and the second is a fuzzy control-inferencing algorithm. The fuzzy identification-learning algorithm updates the membership functions on the action side of the rules and the fuzzy control-inferencing algorithm calculates fuzzy control data. This approach guarantees the stability, convergence, and robustness of the closed-loop feedback system.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"2 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":"128250089","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: 30
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