Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583691
T. Yin, C.S.G. Lee
A fuzzy inference system (FIS), called characteristic point based fuzzy inference system (CPFIS), is proposed to model the input output relationship of a complex system. It is observed that the inference operations of FISs are based on the interpolations among the fuzzy rules which emulate the summarizing ability of human beings. The proposed CPFIS provides a systematic method to constructing FISs via the interpolation property with two distinct features: maximum and minimum fuzzy rules which are related to the interpolation property of human reasoning, and their employment for the interpolation property, resulting in a small sized fuzzy rule base for high dimensional systems.
{"title":"A characteristic-point-based fuzzy inference system","authors":"T. Yin, C.S.G. Lee","doi":"10.1109/AFSS.1996.583691","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583691","url":null,"abstract":"A fuzzy inference system (FIS), called characteristic point based fuzzy inference system (CPFIS), is proposed to model the input output relationship of a complex system. It is observed that the inference operations of FISs are based on the interpolations among the fuzzy rules which emulate the summarizing ability of human beings. The proposed CPFIS provides a systematic method to constructing FISs via the interpolation property with two distinct features: maximum and minimum fuzzy rules which are related to the interpolation property of human reasoning, and their employment for the interpolation property, resulting in a small sized fuzzy rule base for high dimensional systems.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117293732","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583544
I.I. Flokos
This paper presents a new method for fuzzy pattern classification. Its main difference from other methods is the introduction of a measure of similarity between the patterns and the point in the n-dimensional space which is to be classified. Furthermore this method is robust against noise corrupted patterns, which is an important aspect in many pattern classification problems.
{"title":"A new method for fuzzy pattern classification based on measures of similarity","authors":"I.I. Flokos","doi":"10.1109/AFSS.1996.583544","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583544","url":null,"abstract":"This paper presents a new method for fuzzy pattern classification. Its main difference from other methods is the introduction of a measure of similarity between the patterns and the point in the n-dimensional space which is to be classified. Furthermore this method is robust against noise corrupted patterns, which is an important aspect in many pattern classification problems.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706186","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583590
K. Lou, Chrong-Yan Kuo, L. Sheu
In this work, we are concerned with the use of the fuzzy inference mechanism in order to develop a novel on-line fuzzy self-tuning PID control scheme for improving the performance of the traditional PID controller. We first pre-tune a controlled system to determine its first order plus dead time (FOPDT) model and the steady state control signal (u/sub s/). Then, the reference integral term can be set as (u/sub s///spl int/edt) since the reference integral control signal is equal to steady state control signal. Based on the control error, its first difference and some information of the controlled system (the coefficients of the FOPDT model), the fuzzy inference mechanism will be used to determine the proportional term, the increment of the integral term and the derivative term in the final step. Several numerical examples are given to demonstrate the feasibility of the proposed method in this paper.
{"title":"A novel method for fuzzy self-tuning PID controllers","authors":"K. Lou, Chrong-Yan Kuo, L. Sheu","doi":"10.1109/AFSS.1996.583590","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583590","url":null,"abstract":"In this work, we are concerned with the use of the fuzzy inference mechanism in order to develop a novel on-line fuzzy self-tuning PID control scheme for improving the performance of the traditional PID controller. We first pre-tune a controlled system to determine its first order plus dead time (FOPDT) model and the steady state control signal (u/sub s/). Then, the reference integral term can be set as (u/sub s///spl int/edt) since the reference integral control signal is equal to steady state control signal. Based on the control error, its first difference and some information of the controlled system (the coefficients of the FOPDT model), the fuzzy inference mechanism will be used to determine the proportional term, the increment of the integral term and the derivative term in the final step. Several numerical examples are given to demonstrate the feasibility of the proposed method in this paper.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"383 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132013230","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583600
Shyh Hwang, Jung-Jae Chao
A /spl Delta/~-composition (i.e. /spl forall//spl dot/-A/spl I.udot/-composition) is generated from Mizumoto's (1983) /spl Delta/-composition, and an algorithm using Mandani's fuzzy implication (R) is proposed to describe the system operation. Computer simulation is performed on Box and Jenkin's (1970) gas furnace data. The identified fuzzy model is compared with Zadeh's max-min algorithm.
{"title":"An identification algorithm in fuzzy relational systems","authors":"Shyh Hwang, Jung-Jae Chao","doi":"10.1109/AFSS.1996.583600","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583600","url":null,"abstract":"A /spl Delta/~-composition (i.e. /spl forall//spl dot/-A/spl I.udot/-composition) is generated from Mizumoto's (1983) /spl Delta/-composition, and an algorithm using Mandani's fuzzy implication (R) is proposed to describe the system operation. Computer simulation is performed on Box and Jenkin's (1970) gas furnace data. The identified fuzzy model is compared with Zadeh's max-min algorithm.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122384957","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583673
Cui Hongbin, Zheng Chongyon
The authors introduce the concept of stratification structures on completely distributive lattices by direct product decompositions of completely distributive lattices, and prove that there is, up to isomorphism, a unique stratification structure on any normal completely distributive lattice. They then give the concept of stratified completely distributive lattices and prove that the category of stratified completely distributive lattices and stratification-preserving homomorphisms is equivalent to the category whose objects are completely distributive lattices of the form L/sup X/, where L is an irreducible completely distributive lattice and L/sup X/ denotes the family of all L-fuzzy sets on a non-empty set X, and whose morphisms are bi-induced maps. As an application of these results, they give a definition of compactness which has the character of stratifications for a kind of topological molecular lattices.
{"title":"Stratification structures on a kind of completely distributive lattices and their applications in theory of topological molecular lattices","authors":"Cui Hongbin, Zheng Chongyon","doi":"10.1109/AFSS.1996.583673","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583673","url":null,"abstract":"The authors introduce the concept of stratification structures on completely distributive lattices by direct product decompositions of completely distributive lattices, and prove that there is, up to isomorphism, a unique stratification structure on any normal completely distributive lattice. They then give the concept of stratified completely distributive lattices and prove that the category of stratified completely distributive lattices and stratification-preserving homomorphisms is equivalent to the category whose objects are completely distributive lattices of the form L/sup X/, where L is an irreducible completely distributive lattice and L/sup X/ denotes the family of all L-fuzzy sets on a non-empty set X, and whose morphisms are bi-induced maps. As an application of these results, they give a definition of compactness which has the character of stratifications for a kind of topological molecular lattices.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349334","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583655
Huang Chongfu
A basic problem in natural disaster management is that disastrous samples are too small to be used for risk assessment using pure probabilistic methods. In this paper, the information diffusion method relevant to fuzzy information analysis is introduced for processing small samples. A reliable probability distribution can be formulated directly from incomplete fuzzy information of the small sample. An example is discussed. Results show that this method is effective for natural disaster risk assessment.
{"title":"Using a fuzzy method to study a small sample problem of natural disaster risk assessment","authors":"Huang Chongfu","doi":"10.1109/AFSS.1996.583655","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583655","url":null,"abstract":"A basic problem in natural disaster management is that disastrous samples are too small to be used for risk assessment using pure probabilistic methods. In this paper, the information diffusion method relevant to fuzzy information analysis is introduced for processing small samples. A reliable probability distribution can be formulated directly from incomplete fuzzy information of the small sample. An example is discussed. Results show that this method is effective for natural disaster risk assessment.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124522687","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583626
N. Zhong, S. Ohsuga
This paper introduces a new approach for rule discovery from databases, in which a variation of transition matrix named generalizations distribution table (GDT) is used as a hypothesis search space for generalization. Furthermore, by representing the GDT as connectionist networks, if-then rules can be discovered in an evolutionary, parallel-distributed cooperative mode. The key features of this approach are that it can predict unseen instances because the search space considers all possible combination of the seen instances, and the uncertainty of a rule including the prediction of possible instances can be explicitly represented in the strength of the rule. This paper focuses on some basic concepts of our methodology and how to represent generalizations distribution tables by connectionist networks.
本文提出了一种新的从数据库中发现规则的方法，该方法将转换矩阵的变体GDT (generalization distribution table)作为假设搜索空间进行泛化。此外，通过将GDT表示为连接主义网络，可以在进化的、并行分布的合作模式中发现if-then规则。这种方法的关键特点是，它可以预测未见的实例，因为搜索空间考虑了所有可能的实例组合，并且规则的不确定性包括可能实例的预测可以显式地表示在规则的强度中。本文重点讨论了我们的方法的一些基本概念，以及如何用连接网络表示泛化分布表。
{"title":"Representing a generalizations distribution table by connectionist networks for evolutionary rule discovery","authors":"N. Zhong, S. Ohsuga","doi":"10.1109/AFSS.1996.583626","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583626","url":null,"abstract":"This paper introduces a new approach for rule discovery from databases, in which a variation of transition matrix named generalizations distribution table (GDT) is used as a hypothesis search space for generalization. Furthermore, by representing the GDT as connectionist networks, if-then rules can be discovered in an evolutionary, parallel-distributed cooperative mode. The key features of this approach are that it can predict unseen instances because the search space considers all possible combination of the seen instances, and the uncertainty of a rule including the prediction of possible instances can be explicitly represented in the strength of the rule. This paper focuses on some basic concepts of our methodology and how to represent generalizations distribution tables by connectionist networks.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126559524","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583685
Sung-May Hsu
We propose a novel version on the study of consumer involvement. Mathematical definitions for the consumer involvement and the degree of consumer involvement is created to replace the traditional semantic definitions so that a single synthetic index ranged in [0,1] can be manipulated to measure the degree of multi facet consumer involvement, which is objective and obvious.
{"title":"A fuzzy mathematical approach for measuring multi-facet consumer involvement","authors":"Sung-May Hsu","doi":"10.1109/AFSS.1996.583685","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583685","url":null,"abstract":"We propose a novel version on the study of consumer involvement. Mathematical definitions for the consumer involvement and the degree of consumer involvement is created to replace the traditional semantic definitions so that a single synthetic index ranged in [0,1] can be manipulated to measure the degree of multi facet consumer involvement, which is objective and obvious.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643018","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583554
Ching-Hong Lee, C. Teng
In this paper we present a fuzzy neural network (FNN) for controlling single-input single-output nonlinear affine system. The proposed fuzzy neural network controller feedback linearizes the nonlinear control system. The tracking performance is achieved by a state feedback controller based on the fuzzy neural network, even though the nonlinear system is unknown.
{"title":"Control of nonlinear systems using a Fuzzy Neural Network","authors":"Ching-Hong Lee, C. Teng","doi":"10.1109/AFSS.1996.583554","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583554","url":null,"abstract":"In this paper we present a fuzzy neural network (FNN) for controlling single-input single-output nonlinear affine system. The proposed fuzzy neural network controller feedback linearizes the nonlinear control system. The tracking performance is achieved by a state feedback controller based on the fuzzy neural network, even though the nonlinear system is unknown.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133330162","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583663
J. Kacprzyk
The author discusses computer support of consensus reaching in a group of individuals under fuzzy preferences and a fuzzy majority, using a group DSS (GDSS), and supervised by a "super-individual", a moderator, who monitors and runs the process. For measuring how far the group is from "consensus", Kacprzyk and Fedrizzi's (1986, 1988, 1989, 1995) soft degree of consensus is employed viewed as a degree to which, say, most of the important individuals agree to almost all of the relevant options. Yager's (1988, 1996) ordered weighted averaging (OWA) operators for importance qualified data are used.
{"title":"Supporting consensus reaching under fuzziness via ordered weighted averaging (OWA) operators","authors":"J. Kacprzyk","doi":"10.1109/AFSS.1996.583663","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583663","url":null,"abstract":"The author discusses computer support of consensus reaching in a group of individuals under fuzzy preferences and a fuzzy majority, using a group DSS (GDSS), and supervised by a \"super-individual\", a moderator, who monitors and runs the process. For measuring how far the group is from \"consensus\", Kacprzyk and Fedrizzi's (1986, 1988, 1989, 1995) soft degree of consensus is employed viewed as a degree to which, say, most of the important individuals agree to almost all of the relevant options. Yager's (1988, 1996) ordered weighted averaging (OWA) operators for importance qualified data are used.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384305","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}
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