716 resultados para fuzzy rule interpolation
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The paper presents a competence-based instructional design system and a way to provide a personalization of navigation in the course content. The navigation aid tool builds on the competence graph and the student model, which includes the elements of uncertainty in the assessment of students. An individualized navigation graph is constructed for each student, suggesting the competences the student is more prepared to study. We use fuzzy set theory for dealing with uncertainty. The marks of the assessment tests are transformed into linguistic terms and used for assigning values to linguistic variables. For each competence, the level of difficulty and the level of knowing its prerequisites are calculated based on the assessment marks. Using these linguistic variables and approximate reasoning (fuzzy IF-THEN rules), a crisp category is assigned to each competence regarding its level of recommendation.
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Propuesta de reconocimiento del estándar de comodidad en clientes con pénfigo vulgar utilizando la Lógica FuzzyO objetivo é propor a Lógica Fuzzy para reconhecimento de padrões de conforto de pessoas submetidas a uma tecnologia de cuidar em Enfermagem por apresentarem pênfigo vulgar, uma doença cutâneo-mucosa rara que acomete principalmente adultos. A proposta aplicável em métodos experimentais com sujeitos submetidos à comparação quali-quantitativa (taxonomia/pertinência) do padrão de conforto antes e depois da intervenção. Requer o registro em escala cromática correspondente à intensidade de cada atributo: dor; mobilidade e comprometimento da autoimagem. As regras Fuzzy estabelecidas pela máquina de inferência definem o padrão de conforto em desconforto máximo, mediano e mínimo, traduzindo a eficácia dos cuidados de Enfermagem. Apesar de pouco utilizada na área de Enfermagem, essa lógica viabiliza pesquisas sem dimensionamento a priori do número de sujeitos em função da estimação de parâmetros populacionais. Espera-se avaliação do padrão de conforto do cliente com pênfigo diante da tecnologia aplicada de forma personalizada, conduzindo a avaliação global.
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When continuous data are coded to categorical variables, two types of coding are possible: crisp coding in the form of indicator, or dummy, variables with values either 0 or 1; or fuzzy coding where each observation is transformed to a set of "degrees of membership" between 0 and 1, using co-called membership functions. It is well known that the correspondence analysis of crisp coded data, namely multiple correspondence analysis, yields principal inertias (eigenvalues) that considerably underestimate the quality of the solution in a low-dimensional space. Since the crisp data only code the categories to which each individual case belongs, an alternative measure of fit is simply to count how well these categories are predicted by the solution. Another approach is to consider multiple correspondence analysis equivalently as the analysis of the Burt matrix (i.e., the matrix of all two-way cross-tabulations of the categorical variables), and then perform a joint correspondence analysis to fit just the off-diagonal tables of the Burt matrix - the measure of fit is then computed as the quality of explaining these tables only. The correspondence analysis of fuzzy coded data, called "fuzzy multiple correspondence analysis", suffers from the same problem, albeit attenuated. Again, one can count how many correct predictions are made of the categories which have highest degree of membership. But here one can also defuzzify the results of the analysis to obtain estimated values of the original data, and then calculate a measure of fit in the familiar percentage form, thanks to the resultant orthogonal decomposition of variance. Furthermore, if one thinks of fuzzy multiple correspondence analysis as explaining the two-way associations between variables, a fuzzy Burt matrix can be computed and the same strategy as in the crisp case can be applied to analyse the off-diagonal part of this matrix. In this paper these alternative measures of fit are defined and applied to a data set of continuous meteorological variables, which are coded crisply and fuzzily into three categories. Measuring the fit is further discussed when the data set consists of a mixture of discrete and continuous variables.
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In this paper, I consider a general and informationally effcient approach to determine the optimal access rule and show that there exists a simple rule that achieves the Ramsey outcome as the unique equilibrium when networks compete in linear prices without network-based price discrimination. My approach is informationally effcient in the sense that the regulator is required to know only the marginal cost structure, i.e. the marginal cost of making and terminating a call. The approach is general in that access prices can depend not only on the marginal costs but also on the retail prices, which can be observed by consumers and therefore by the regulator as well. In particular, I consider the set of linear access pricing rules which includes any fixed access price, the Efficient Component Pricing Rule (ECPR) and the Modified ECPR as special cases. I show that in this set, there is a unique access rule that achieves the Ramsey outcome as the unique equilibrium as long as there exists at least a mild degree of substitutability among networks' services.
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Studying the geographic variation of phenotypic traits can provide key information about the potential adaptive function of alternative phenotypes. Gloger's rule posits that animals should be dark-vs. light-colored in warm and humid vs. cold and dry habitats, respectively. The rule is based on the assumption that melanin pigments and/or dark coloration confer selective advantages in warm and humid regions. This rule may not apply, however, if genes for color are acting on other traits conferring fitness benefits in specific climes. Covariation between coloration and climate will therefore depend on the relative importance of coloration or melanin pigments and the genetically correlated physiological and behavioral processes that enable an animal to deal with climatic factors. The Barn Owl (Tyto alba) displays three melanin-based plumage traits, and we tested whether geographic variation in these traits at the scale of the North American continent supported Gloger's rule. An analysis of variation of pheomelanin-based reddish coloration and of the number and size of black feather spots in 1,369 museum skin specimens showed that geographic variation was correlated with ambient temperature and precipitation. Owls were darker red in color and displayed larger but fewer black feather spots in colder regions. Owls also exhibited more and larger black spots in regions where the climate was dry in winter. We propose that the associations between pigmentation and ambient temperature are of opposite sign for reddish coloration and spot size vs. the number of spots because selection exerted by climate (or a correlated variable) is plumage trait-specific or because plumage traits are genetically correlated with different adaptations.
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Directed evolution of life through millions of years, such as increasing adult body size, is one of the most intriguing patterns displayed by fossil lineages. Processes and causes of such evolutionary trends are still poorly understood. Ammonoids (externally shelled marine cephalopods) are well known to have experienced repetitive morphological evolutionary trends of their adult size, shell geometry and ornamentation. This study analyses the evolutionary trends of the family Acrochordiceratidae Arthaber, 1911 from the Early to Middle Triassic (251228 Ma). Exceptionally large and bed-rock-controlled collections of this ammonoid family were obtained from strata of Anisian age (Middle Triassic) in north-west Nevada and north-east British Columbia. They enable quantitative and statistical analyses of its morphological evolutionary trends. This study demonstrates that the monophyletic clade Acrochordiceratidae underwent the classical evolute to involute evolutionary trend (i.e. increasing coiling of the shell), an increase in its shell adult size (conch diameter) and an increase in the indentation of its shell suture shape. These evolutionary trends are statistically robust and seem more or less gradual. Furthermore, they are nonrandom with the sustained shift in the mean, the minimum and the maximum of studied shell characters. These results can be classically interpreted as being constrained by the persistence and common selection pressure on this mostly anagenetic lineage characterized by relatively moderate evolutionary rates. Increasing involution of ammonites is traditionally interpreted by increasing adaptation mostly in terms of improved hydrodynamics. However, this trend in ammonoid geometry can also be explained as a case of Copes rule (increasing adult body size) instead of functional explanation of coiling, because both shell diameter and shell involution are two possible paths for ammonoids to accommodate size increase.
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The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.
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Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report I demonstrate the advantages of the fuzzy coding of explanatory variables: first, nonlinear relationships can be diagnosed; second, more variance in the responses can be explained; and third, in the presence of categorical explanatory variables (for example, years, regions) the interpretation of the resulting triplot ordination is unified because all explanatory variables are measured at a categorical level.
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Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.
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1.1 Fundamentals Chest pain is a common complaint in primary care patients (1 to 3% of all consultations) (1) and its aetiology can be miscellaneous, from harmless to potentially life threatening conditions. In primary care practice, the most prevalent aetiologies are: chest wall syndrome (43%), coronary heart disease (12%) and anxiety (7%) (2). In up to 20% of cases, potentially serious conditions as cardiac, respiratory or neoplasic diseases underlie chest pain. In this context, a large number of laboratory tests are run (42%) and over 16% of patients are referred to a specialist or hospitalized (2).¦A cardiovascular origin to chest pain can threaten patient's life and investigations run to exclude a serious condition can be expensive and involve a large number of exams or referral to specialist -‐ often without real clinical need. In emergency settings, up to 80% of chest pains in patients are due to cardiovascular events (3) and scoring methods have been developed to identify conditions such as coronary heart disease (HD) quickly and efficiently (4-‐6). In primary care, a cardiovascular origin is present in only about 12% of patients with chest pain (2) and general practitioners (GPs) need to exclude as safely as possible a potential serious condition underlying chest pain. A simple clinical prediction rule (CPR) like those available in emergency settings may therefore help GPs and spare time and extra investigations in ruling out CHD in primary care patients. Such a tool may also help GPs reassure patients with more common origin to chest pain.
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The paper deals with a bilateral accident situation in which victims haveheterogeneous costs of care. With perfect information,efficient care bythe injurer raises with the victim's cost. When the injurer cannot observeat all the victim's type, and this fact can be verified by Courts, first-bestcannot be implemented with the use of a negligence rule based on thefirst-best levels of care. Second-best leads the injurer to intermediate care,and the two types of victims to choose the best response to it. This second-bestsolution can be easily implemented by a negligence rule with second-best as duecare. We explore imperfect observation of the victim's type, characterizing theoptimal solution and examining the different legal alternatives when Courts cannotverify the injurers' statements. Counterintuitively, we show that there is nodifference at all between the use by Courts of a rule of complete trust and arule of complete distrust towards the injurers' statements. We then relate thefindings of the model to existing rules and doctrines in Common Law and Civil Lawlegal systems.
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The Attorney General’s Consumer Protection Division receives hundreds of calls and consumer complaints every year. Follow these tips to avoid unexpected expense and disappointments. This record is about: Price-Gouging Rule in Effect in Storm- and Flood-damaged Counties
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We study the standard economic model of unilateral accidents, in its simplest form, assumingthat the injurers have limited assets.We identify a second-best optimal rule that selects as duecare the minimum of first-best care, and a level of care that takes into account the wealth ofthe injurer. We show that such a rule in fact maximizes the precautionary effort by a potentialinjurer. The idea is counterintuitive: Being softer on an injurer, in terms of the required level ofcare, actually improves the incentives to take care when he is potentially insolvent. We extendthe basic result to an entire population of potentially insolvent injurers, and find that the optimalgeneral standards of care do depend on wealth, and distribution of income. We also show theconditions for the result that higher income levels in a given society call for higher levels of carefor accidents.
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We study how to promote compliance with rules in everyday situations. Having access to unique data on the universe of users of all public libraries inBarcelona, we test the effect of sending email messages with dierent contents.We find that users return their items earlier if asked to do so in a simple email.Emails reminding users of the penalties associated with late returns are more effective than emails with only a generic reminder. We find differential treatmenteffects by user types. The characteristics we analyze are previous compliance,gender, age, and nationality.
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A biplot, which is the multivariate generalization of the two-variable scatterplot, can be used to visualize the results of many multivariate techniques, especially those that are based on the singular value decomposition. We consider data sets consisting of continuous-scale measurements, their fuzzy coding and the biplots that visualize them, using a fuzzy version of multiple correspondence analysis. Of special interest is the way quality of fit of the biplot is measured, since it is well-known that regular (i.e., crisp) multiple correspondence analysis seriously under-estimates this measure. We show how the results of fuzzy multiple correspondence analysis can be defuzzified to obtain estimated values of the original data, and prove that this implies an orthogonal decomposition of variance. This permits a measure of fit to be calculated in the familiar form of a percentage of explained variance, which is directly comparable to the corresponding fit measure used in principal component analysis of the original data. The approach is motivated initially by its application to a simulated data set, showing how the fuzzy approach can lead to diagnosing nonlinear relationships, and finally it is applied to a real set of meteorological data.