{"title":"Continuous ID3 algorithm with fuzzy entropy measures","authors":"K. Cios, L. Sztandera","doi":"10.1109/FUZZY.1992.258659","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258659","url":null,"abstract":"Fuzzy entropy measures are used to obtain a quick convergence of a continuous ID3 (CID3) algorithm proposed by K.J. Cios and N. Liu (1991), which allows for self-generation of a hierarchical feedforward neural network architecture by converting decision trees into hidden layers of a neural network. To demonstrate the learning capacity of the fuzzy version of the CID3 algorithm, it was tested on difficult spiral data consisting of 192 points, with 96 points for each spiral. One spiral is generated as a reflection of another, making the problem highly not linearly separable. A remarkable decrease in convergence time is achieved by using a fuzzy entropy measure with generalized Dombi operations.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"140 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":"128694946","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}
J. Cleland, W. Turner, P. Wang, T. Espy, P. J. Chappell, R. Spiegel, B. Bose
{"title":"Fuzzy logic control of AC induction motors","authors":"J. Cleland, W. Turner, P. Wang, T. Espy, P. J. Chappell, R. Spiegel, B. Bose","doi":"10.1109/FUZZY.1992.258768","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258768","url":null,"abstract":"Simulated results of a microprocessor-based fuzzy logic motor controller (FLMC) are described. The investigation includes a motor stator voltage control scheme to minimize motor input power at specified speed/torque conditions, simulation of AC motor performance, and development of a FLMC for optimized motor efficiency. Simulated FLMC results compared favorably with other motor control approaches. Potential savings are quantified on the basis of the preliminary predictions of FLMC performance.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"1 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":"129322761","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}
T. Yamaguchi, K. Goto, T. Takagi, K. Doya, T. Mita
{"title":"Intelligent control of a flying vehicle using fuzzy associative memory system","authors":"T. Yamaguchi, K. Goto, T. Takagi, K. Doya, T. Mita","doi":"10.1109/FUZZY.1992.258718","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258718","url":null,"abstract":"The authors propose a flying vehicle intelligent control system which simulates the pilots operation knowledge and training steps. To simulate the operation knowledge and training steps, a fuzzy associative memory system called FAMOUS was used. FAMOUS uses associative memory neural networks which represent fuzzy knowledge. There are two types of operation knowledge; dynamic fuzzy knowledge, for example the circular flight operation pattern; and static fuzzy knowledge, for example the hovering operation model corresponding to each flying condition. FAMOUS represents both dynamic and static fuzzy knowledge using its hierarchical knowledge representation. The implementation of the intelligent control system using FAMOUS and the realization of hovering flight and circular flight are discussed.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"22 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":"129351558","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}
{"title":"Eigenvectors and generators of fuzzy relations","authors":"J. Jacas, J. Recasens","doi":"10.1109/FUZZY.1992.258741","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258741","url":null,"abstract":"A new geometric approach to the study of the eigenvectors is provided. The T-eigenvectors of a T-indistinguishability operator are characterized as its generators in the sense of the representation theorem of L. Valverde (1985). This theorem states that every T-indistinguishability operator on a set X can be generated by a family of fuzzy subsets of X and, reciprocally, every family of fuzzy subsets of X generated a T-indistinguishability operator on X in a natural way. Some concepts related to T-eigenvectors and generators of T-indistinguishabilities are reviewed, and their relation is studied. Some examples are given.<<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":"127252627","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}
{"title":"A possibilistic interpretation of fuzzy sets by the context model","authors":"J. Gebhardt, R. Kruse","doi":"10.1109/FUZZY.1992.258699","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258699","url":null,"abstract":"The context model provides a formal framework for the representation, interpretation, and analysis of vague and uncertain data. The authors apply the context model to clarify the numerical foundations of fuzzy set theory and some well-known concepts like the extension principle. They introduce the semantic background of the context model, especially the concept of valuated vague characteristics. Some relationships between the context model and possibility theory are discussed. The epistemic and physical interpretation of fuzzy sets is dealt with by the context model.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"16 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":"125598000","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}
{"title":"A fuzzy knowledge-based system for predicting surface roughness in finish turning","authors":"J. Fei, I. Jawahir","doi":"10.1109/FUZZY.1992.258777","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258777","url":null,"abstract":"A search for a non-deterministic means for predicting the achievable levels of surface roughness in finish turning operations was initiated and this initiative has resulted in a fuzzy knowledge-based system, which can precisely predict the achievable surface roughness levels. The authors outline the complex nature of the finish turning problem, justifying the need for such a predictive system, and present a new methodology for predicting surface roughness values for a given set of cutting conditions, work materials, tool insert types, and tool geometries.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"118 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":"126425535","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}
{"title":"Extending Boolean information retrieval: a fuzzy model based on linguistic variables","authors":"Gloria Bordogna, P. Carrara, G. Pasi","doi":"10.1109/FUZZY.1992.258753","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258753","url":null,"abstract":"In information retrieval systems the vagueness in user requests for information is mainly managed by the use of numeric weights present the formal definition of a retrieval model in which linguistic descriptors are used in the query language both to express the importance that a term must have in the desired documents and to label the retrieved documents in relevance classes. By attaching a numeric weight to a term, a user provides a quantitative description of the importance of that term in the documents sought. If the introduction of weights reduces the vagueness in query formulation, the use of numeric weights requires a clear knowledge of their semantics and the translation of a fuzzy concept in a precise numeric value. Based on these problems and starting from an existing weighted Boolean retrieval model, the authors formalize within fuzzy set theory a new model that allows the interpretation of a user query in which a linguistic descriptor is attached to each term.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"83 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":"123097266","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}
{"title":"Fuzzy information systems","authors":"F. Petry, B. Buckles, A. Yazici, R. George","doi":"10.1109/FUZZY.1992.258724","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258724","url":null,"abstract":"The authors discuss the issue of uncertainty management in conceptual, logical, and navigational data models. Two different approaches to conceptual modeling are examined, the extended IFO model and the extended entity relationship model. These models can represent complex objects with uncertainty in the attribute values chiefly with their powerful abstraction mechanisms. The logical models, specifically the first normal form (1NF) model and the non-1NF data model, are also discussed in detail. The question of representing and manipulating impression in navigational models, i.e. network and object-oriented data models, is considered.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"83 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":"126020645","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}
{"title":"Flute: fuzzy learning in unfamiliar teacher environments","authors":"B. Dasarathy","doi":"10.1109/FUZZY.1992.258802","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258802","url":null,"abstract":"Two conceptually elegant ideas of learning with an unfamiliar teacher and fuzzy models are synergistically combined to derive a new fuzzy pattern recognition methodology for operating in imperfectly supervised environments. Under this new methodology, which is applicable to, multiple pattern class problems also, the fuzziness introduced into the recognition problem by the imperfectness of the supervision in the environment is modeled with fuzzy membership functions. These functions are learnt during the training phase by employing the unfamiliar teacher concepts, and deployed during the ensuing classification phase to take into account the imperfectness of the learning environment.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"9 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":"114135278","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}
Toshio Fukuda, Koji Shimojima, F. Arai, H. Matsuura
{"title":"Multi-sensor integration system based on fuzzy inference and neural network for industrial application","authors":"Toshio Fukuda, Koji Shimojima, F. Arai, H. Matsuura","doi":"10.1109/FUZZY.1992.258778","DOIUrl":"https://doi.org/10.1109/FUZZY.1992.258778","url":null,"abstract":"The authors deal with a multi-sensor system applied to an unknown curved metal surface cutting robot system. The measurements were performed by sensors set on an array of the tip of a five axis manipulator. The sensor array is carried to the target surface by moving the manipulator. The manipulator approaches the surface by using sensor outputs. To approach the work fast, the system should use long measurement range sensors. For precise cutting and a fast approach, the system should use both high accuracy sensors and long measurement range sensors. To use these sensors effectively, the multi-sensor integration system was based on neural network and fuzzy inference techniques. As a result, the system can consider the angle between the sensors and the object. The proposed system was shown to be effective through extensive experiments.<<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":"122931433","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}