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.
Fuzzy logic control emerged in the mid 1970s as a paradigm for rule based control of dynamic systems, primarily in process type control situations. In the past two decades, and specially in the mid 80s to mid 90s, fuz...
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Fuzzy logic control emerged in the mid 1970s as a paradigm for rule based control of dynamic systems, primarily in process type control situations. In the past two decades, and specially in the mid 80s to mid 90s, fuzzy logic control has been successfully deployed in a broad spectrum of application areas ranging from automatic train operation to flight systems. Nonetheless there remains a great deal of skepticism within the control community as to what the real merits of this paradigm are. In this paper we focus on the positive aspect of fuzzy logic control, as well as some significant controversial issues that surround this paradigm. In particular, we contend that while the semantic transparency offered by fuzzy logic enables control engineers to efficiently develop control strategies in application areas marked by low order dynamics with weak nonlinearities, the broader impact of fuzzy logic should be in hierarchically structured systems, perhaps in combination with conventional control techniques. We offer examples of application of fuzzy logic in hierarchal setting and discuss a number of theoretical issues that arise in these settings.
there has been a growing body of literature suggesting that some of the problems faced by software development project managers can be at least partially overcome by using fuzzy logic techniques. However, one issue th...
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there has been a growing body of literature suggesting that some of the problems faced by software development project managers can be at least partially overcome by using fuzzy logic techniques. However, one issue that has been generally overlooked in this recommendation is the means by which these `software metricians' can collect data for, develop, and interpret fuzzy logic models in practice. We describe a freely available system that has been built withthis in mind called FULSOME (FUzzy logic for SOftware MEtrics). While there are many tools available for developing fuzzy models, it is suggested that before there will be real adoption of such techniques by project managers there will need to be suitable tools that support their particular work-flows and that use appropriate terminology. Another requirement will be the development of some standard procedures and definitions for such models. Issues involved with membership function elicitation and extraction are also discussed as a first step towards this second goal.
A new implication operator is proposed, based on fuzzy entailment. Using a kernel and new measures of distance between fuzzy sets, it generates a set of fuzzy conclusions of various degrees of plausibility, rather tha...
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A new implication operator is proposed, based on fuzzy entailment. Using a kernel and new measures of distance between fuzzy sets, it generates a set of fuzzy conclusions of various degrees of plausibility, rather than a single fuzzy conclusion. this set of plausible conclusions generates an envelope of plausibility, which is also a fuzzy set. the kernel can be chosen to make the envelope assume any reasonable shape surrounding the rule output, e.g., a Gaussian curve, or a set with a finite support, or the output of the Zadeh implication operator. As operator input becomes increasingly dissimilar to rule input, the envelope spreads further outside the rule output. the kernel can also be chosen to encode assumptions about system response, such as the degree of predictability/nonlinearity, the fractal dimension and the maximum possible rate of change of the output with respect to the input. the new distance functions permit speculative conclusions even when operator input does not intersect rule input. Since the operator's output is speculative, an analogous assumption-free belief envelope is also defined for comparison.
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