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Authors: Yager, Ronald R.

Article Type: Research Article

Abstract: Knowledge representation plays a fundamental role in the construction of artificial intelligence for modeling commonsense reasoning. We emphasize the role fuzzy logic can play in extending some of the models of AI. Some basic ideas from the fuzzy set based theory of approximate reasoning are introduced. We then discuss two extensions of the theory of approximate reasoning, possibilistic logic and effectation logic.

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 27-45, 1999

Authors: Yager, Ronald R. | Kelman, Antoine

Article Type: Research Article

Abstract: We present a general way to select an optimal alternative when there is some uncertainty about the state of the world. It is shown how this method can be applied whatever the uncertainty representation used to model the information about the state of nature: probability distribution, fuzzy set, possibility distribution, a Dempster–Shafer belief structure, or a fuzzy Dempster–Shafer structure. In this general formulation, we show the role of the OWA operators to model the decision maker's attitude.

DOI: 10.3233/IFS-1995-3407

Citation: Journal of Intelligent and Fuzzy Systems, vol. 3, no. 4, pp. 317-323, 1995

Authors: Yager, Ronald R. | Kreinovich, Vladik

Article Type: Research Article

Abstract: In this paper, we develop a relationship between two approaches to combining evidence: the Ordered Abelian Group (OAG) approach and the uninorm approach. We show that while there exist uninorms that are not extended OAG's it turns out that for operations which are continuous (in some reasonable sense), these two approaches coincide.

Citation: Journal of Intelligent & Fuzzy Systems, vol. 14, no. 1, pp. 7-12, 2003

Authors: Rasmussen, Dan | Yager, Ronald R.

Article Type: Research Article

Abstract: The increasing use of computers for transactions and communication have created mountains of data that contain potentially valuable knowledge. To search for this knowledge we have to develop a new generation of tools, which have the ability of flexible querying and intelligent searching. In this paper we will introduce an extension of a fuzzy query language called Summary SQL which can be used for knowledge discovery and data mining. We show how it can be used to search for fuzzy rules.

Keywords: Knowledge discovery, Data mining, Fuzzy query, Linguistic summary, Fuzzy rules

DOI: 10.3233/IDA-1997-1104

Citation: Intelligent Data Analysis, vol. 1, no. 1, pp. 49-58, 1997

Authors: Yager, Ronald R. | Kelman, Antoine

Article Type: Research Article

Abstract: The Analytical Hierarchy Process (AHP) provides a comprehensive methodology for the solution of multicriteria decision problems which makes considerable use of comparison generated importances to help in the aggregation of lower order concepts in the formulation of higher order concepts. We introduce an extension of the Analytical Hierarchy Process using the Ordered Weighted Averaging (OWA) operators. This extension, which generalizes the aggregation process used in the AHP, allows more flexibility in the formulation of higher order concepts and provides the AHP an even greater facility for modeling human decision making. Using the OWA operators we are able to model situations …where the number of sub-criteria needed to satisfy a higher order concept can be expressed in terms of linguistic quantifiers. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 401-417, 1999

Authors: Senapati, Tapan | Yager, Ronald R.

Article Type: Research Article

Abstract: Fermatean fuzzy sets (FFSs), proposed by Senapati and Yager (2019a), can handle uncertain information more easily in the process of decision making. They defined basic operations over the Fermatean fuzzy sets. Here we shall introduce three new operations: subtraction, division, and Fermatean arithmetic mean operations over Fermatean fuzzy sets. We discuss their properties in details. Later, we develop a Fermatean fuzzy weighted product model to solve the multi-criteria decision-making problem. Finally, an illustrative example of selecting a suitable bridge construction method is given to verify the approach developed by us and to demonstrate its practicability and effectiveness.

Keywords: Fermatean fuzzy set, subtraction operation, division operation, Fermatean arithmetic mean operation, multiple criteria decision making (MCDM), weighted product model (WPM)

Citation: Informatica, vol. 30, no. 2, pp. 391-412, 2019

Authors: Lee, Chul-Heui | Yager, Ronald R.

Article Type: Research Article

Abstract: A new variable step size least mean squares (LMS) algorithm using fuzzy logic (FVSS-LMS) is presented. The change of the step size at each iteration, which increases or decreases according to the degree of misadaption, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed while preserving low misadjustment. The norm of the cross correlation between the estimation error and input data is used as a measure of misadaption. Simulation results are presented to verify the performance of the proposed algorithm.

DOI: 10.3233/IFS-1996-4202

Citation: Journal of Intelligent and Fuzzy Systems, vol. 4, no. 2, pp. 101-106, 1996

Authors: Yager, Ronald R. | Filev, Dimitar P.

Article Type: Research Article

Abstract: We develop, based upon the mountain clustering method, a procedure for learning fuzzy systems models from data. First we discuss the mountain clustering method. We then show how it could be used to obtain the structure of fuzzy systems models. The initial estimates of this model are obtained from the cluster centers. We then use a back propagation algorithm to tune the model.

DOI: 10.3233/IFS-1994-2301

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 3, pp. 209-219, 1994

Authors: Yager, Ronald R. | Filev, Dimitar P.

Article Type: Research Article

Abstract: A methodology is suggested here for the development of fuzzy systems models that is a combination of the AI-expert systems approach, with its heavy dependence on the use of expert knowledge, and the neural network-type systems building, with its emphasis on learning from data observations. We use expert-provided information in the form of template linguistic values to induce potential elemental rules for the knowledge base of the system model. We then introduce input-output observations into a simple learning mechanism to obtain weights characterizing the effect of each of the potential elemental rules on the overall systems model. The development of …the learning mechanism is based on a representation of systems models combining fuzzy logic and Dempster-Shafer theory, which we previously introduced. Show more

DOI: 10.3233/IFS-1994-2103

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 39-54, 1994

Authors: Jin, LeSheng | Chen, Zhen-Song | Yager, Ronald R. | Langari, Reza

Article Type: Research Article

Abstract: This letter reports a new type of uncertain information that is different from some well known existing uncertain information, such as probability information, fuzzy information, interval information and basic uncertain information. This type of uncertain information allows some specified compromise in interacting decision environments and gives some acceptance area when facing with uncertainties. We firstly introduce the cognitive interval information and then naturally propose the cognitive uncertain information as an extension. The featured acceptance area provides more flexibility in uncertain information handling and it can be regarded as some specified uncertain range (versus the certainty degree in basic uncertain information). …The new proposals have advantages in some uncertain decision making scenarios where intersubjectivity and interaction of decision makers play important roles. Besides, some basic structural properties are briefly discussed. Moreover, some motivational examples are presented to show its usage in group decision making to help automatically obtain consistency or consensus in aggregating the different individual evaluations. Show more

Keywords: Cognitive interval information, cognitive uncertain information, decision making, group decision making, information fusion, uncertain information

DOI: 10.3233/JIFS-223119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9411-9418, 2023