the family of ordered weighted averaging (OWA) operators, as introduced by R.R. Yager (1988), appears to be very useful in flexible query answering, including information retrieval, where the information needs are oft...
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the family of ordered weighted averaging (OWA) operators, as introduced by R.R. Yager (1988), appears to be very useful in flexible query answering, including information retrieval, where the information needs are often modeled by an aggregation (of the criteria in the query) between /spl and/ (pure AND) and V (pure OR). In this paper, we discuss and set up a general set of requirements for importance-weighted aggregation, and show that an importance weighting scheme suggested by Yager (1977) for OWA operators satisfies the requirements. the weighted arithmetic mean is shown to be order-equivalent to the special case of importance-weighted OWA operators where the importance-weighted satisfaction of the criteria are weighted evenly in the OWA aggregation.
this paper presents the application of adaptive fuzzy logic systems to model electric arc furnaces. the main objectives are to provide the rationale and to justify the use of fuzzy modeling for electric furnaces. this...
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this paper presents the application of adaptive fuzzy logic systems to model electric arc furnaces. the main objectives are to provide the rationale and to justify the use of fuzzy modeling for electric furnaces. this is done with reference to three important properties of fuzzy logic systems, namely, nonlinear black-box modeling capability, universal approximation ability and the functional equivalence with radial basis function networks. the detailed investigation regarding the application of adaptive fuzzy logic systems to electric arc furnace modeling is presented. It will be demonstrated that the application of adaptive fuzzy logic systems as a non-parametric system identification method to model nonlinear systems can be considered as an alternative to artificial neural network. the proposed modeling methods are described, and their use is illustrated using the actual recorded data.
the paper presents a novel uncertainty framework for rule induction from examples: a fuzzy probabilistic framework. the main motivation for this framework stems from the problem of data fit vs. mental fit in knowledge...
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the paper presents a novel uncertainty framework for rule induction from examples: a fuzzy probabilistic framework. the main motivation for this framework stems from the problem of data fit vs. mental fit in knowledge acquisition for decision support systems. the framework is based on an extension of the probability of a fuzzy event (as defined by L.A. Zadeh, 1968)), and is highly suitable for learning and reasoning with uncertainty. Experiments show that the framework results in a simple rule base by which highly accurate classifications are obtained and explained.
the paper discusses the semantics of some basic extensions of the concept of a functional dependency when fuzzy sets are used. Extended FDs are especially studied from a knowledge discovery point of view. Two types of...
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the paper discusses the semantics of some basic extensions of the concept of a functional dependency when fuzzy sets are used. Extended FDs are especially studied from a knowledge discovery point of view. Two types of extensions are considered. the first one is based on a relaxation of equality into resemblance. the second one is based on a modification of the granularity of the domains and the use of gradual rules. A mining algorithm suited to the discovery of extended FDs of the latter sort is outlined, and the interest of the rules discovered is then questioned. It is shown that, in some cases, additional information can be extracted from the obtained set of rules.
Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views s...
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Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views specificity as the degree to which a possibility distribution points to a single element in the universe of discourse (U). Specificity in relation to U may complicate its utility in the optimization of fuzzy models in their linguistic space. An increase in granularity is useful to measure the amount of information contained in a possibility distribution in relation to fuzzy sets as opposed to U. this abstracted view of specificity motivates the need for a more generalized version of specificity, denoted linguistic specificity (Sp/sub L/), where alternatives are measured in relation to the linguistic terms. Such a generalization is useful in support of automating decisions in a fuzzy domain. Results of the linguistic specificity measure are illustrated using an automobile fuel consumption example.
Although fuzzy numbers (including fuzzy intervals) are often used to capture semantic ambiguity, they are also useful to represent and propagate measurement error. In this application, a classification scheme used by ...
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Although fuzzy numbers (including fuzzy intervals) are often used to capture semantic ambiguity, they are also useful to represent and propagate measurement error. In this application, a classification scheme used by international authorities for assigning biological species into categories of relative endangerment is generalized to accept intervals and triangular or trapezoidal fuzzy numbers as inputs representing empirical estimates of unknown quantities. Non-traditional definitions for fuzzy magnitude comparisons and logical operations were required but, otherwise, standard fuzzy arithmetic was used. A defuzzification step, which explicitly reveals the analyst's attitudes regarding evidence, can condense the result from the fuzzified classification scheme to a single category. But this step is not required and may be counterproductive.
Computational theory of perceptions (CTP) deal with descriptions of perceptions expressed in a natural language. thus, propositions in a natural language play the role of surrogates of perceptions. In CTP, meaning rep...
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Computational theory of perceptions (CTP) deal with descriptions of perceptions expressed in a natural language. thus, propositions in a natural language play the role of surrogates of perceptions. In CTP, meaning representation is a function of what is referred to as constraint-centered semantics of natural languages (CSNL). CSNL is a key component of CTP. A generalized constraint concept plays a key role in CSNL. the collection of generalized constraints and associated rules governing constraint propagation constitute the generalized constraint language (GCL). the rules of constraint propagation in CTP coincide withthe rules of inference in fuzzy logic. the principal rule is the generalized extension principle.
Professional judgement is an integral part of risk assessment, and an important tool in rationalizing risk management. In order to exercise professional judgement, corporate decision makers make intuitive assumptions ...
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Professional judgement is an integral part of risk assessment, and an important tool in rationalizing risk management. In order to exercise professional judgement, corporate decision makers make intuitive assumptions about the complex behaviour of businesses and corporations. they implicitly assume (1) a degree of continuity, and (2) the possibility of discontinuities. the informal, professional reasoning that follows from these assumptions is described. then, the author uses the two fundamental principles of self organization from chaos theory (self similarity, and sensitivity-to-prevailing-conditions), to make these assumptions explicit. Finally, reference is made to a fuzzy implication operator that captures mathematically the informal reasoning inherent in professional judgement. It is argued that the mathematical tools that embody the new operator can make the everyday exercise of professional judgement in risk assessment more consistent and rigorous.
Today's information technology undergoes dramatic mass changes due to urgent requirements like the Year 2000 problem, the European currency union and emerging technologies like the World Wide Web. Many older indus...
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Today's information technology undergoes dramatic mass changes due to urgent requirements like the Year 2000 problem, the European currency union and emerging technologies like the World Wide Web. Many older industrial database applications do not meet these requirements. Often, such legacy databases (LDB) have evolved over several generations of programmers and lack a sufficient technical documentation. However, this documentation is a prerequisite to migrate LDBs to new technologies. Recovering this documentation is a complex task that requires a high amount of expert knowledge and coordination. Computer-aided reverse engineering (CARE) tools have a great potential to reduce this complexity. Still, a practical limitation of current CARE tools is their lack of customizability. In most cases, reverse engineering (RE) knowledge is hardly adaptable, because it has been implemented in general-purpose programming languages. We outline an approach that overcomes this limitation. We introduce a dedicated formalism to specify uncertain RE knowledge. Furthermore, we adapt the backpropagation algorithm to obtain a CARE tool that automatically adjusts the confidences of the implemented RE knowledge to its current application context.
Current methods for data integration and decision support in submarine combat systems do not adequately account for uncertainty in an automated fashion, hence continuing a heavy reliance on operator manipulation of in...
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Current methods for data integration and decision support in submarine combat systems do not adequately account for uncertainty in an automated fashion, hence continuing a heavy reliance on operator manipulation of input data and human interpretation of output information. Fuzzy logic offers an enabling technology for automated uncertainty management in the data integration process. Application of this technology to the fuzzy characterization of contact speed with uncertain information is demonstrated, and is shown to provide significant improvement in tracking solution quality for the single-leg Target Motion Analysis problem. the uncertainty in the target end-point location is described by an enhanced Area of Uncertainty region, which is obtained through combination of the derived fuzzy range characterization with conventional probabilistic information. Follow-on efforts are in progress to address the issue of modeling uncertainty in the basic structure of a fuzzy logic system.
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