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Measures of similarity among fuzzy concepts: A comparative analysis
 Int. J. Approx. Reason
, 1987
"... Many measures of similarity among fuzzy sets have been proposed in the literature, and some have been incorporated into linguistic approximation procedures. The motivations behind these measures are both geometric and settheoretic. We briefly review 19 such measures and compare their performance i ..."
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Cited by 43 (1 self)
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Many measures of similarity among fuzzy sets have been proposed in the literature, and some have been incorporated into linguistic approximation procedures. The motivations behind these measures are both geometric and settheoretic. We briefly review 19 such measures and compare their performance in a behavioral experiment. For crudely categorizing pairs of fuzzy concepts as either "'similar " or "'dissimilar, ""all measures performed well. For distinguishing between degrees of similarity or dissimilarity, certain measures were clearly superior and others were clearly inferior; for a few subjects, however, none of the distance measures adequately modeled their similarity judgments. Measures that account for ordering on the base variable proved to be more highly correlated with subjects " actual similarity judgments. And, surprisingly, the best measures were ones that focus on only one "'slice " of the membership function. Such measures are easiest to compute and may provide insight into the way humans judge similarity among fuzzy concepts.
Mobile Robot Localization Using Fuzzy Maps
, 1996
"... : This paper deals with the problems of map building and mobile robot localization. Ultrasonic sensors information, obtained as the robot moves, is integrated in order to build a map of the environment. This map is afterwards used for robot localization, correcting the errors that the dead reckoning ..."
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Cited by 19 (0 self)
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: This paper deals with the problems of map building and mobile robot localization. Ultrasonic sensors information, obtained as the robot moves, is integrated in order to build a map of the environment. This map is afterwards used for robot localization, correcting the errors that the dead reckoning system accumulates in long displacements. The approach focuses on the way to analyze sensor information and on the way to reconstruct the outline of the objects. Since measuring conditions are unknown and just a small number of observations are normally available to reconstruct the boundaries of the objects, this will generate uncertainty on their real location. Fuzzy sets are used to represent this uncertainty, their degrees of membership indicating the extent to which one boundary can be considered similar to other boundaries located in the proximity. Experimental results with a mobile robot in an office environment are also presented. 1 Introduction In this paper we address the problems...
Obtaining Solutions in Fuzzy Constraint Networks
 International Journal of Approximate Reasoning
, 1997
"... In this work we propose three methods for obtaining solutions in fuzzy constraint networks and study their application to the problem of ordering fuzzy numbers. The techniques proposed may be classified as defuzzification functions which are applicable to any set of mutually dependant fuzzy numbers ..."
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Cited by 16 (2 self)
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In this work we propose three methods for obtaining solutions in fuzzy constraint networks and study their application to the problem of ordering fuzzy numbers. The techniques proposed may be classified as defuzzification functions which are applicable to any set of mutually dependant fuzzy numbers in which the dependence relationships are represented by means of metric constraints. In the paper we suggest the use of these techniques for ordering linked variables in an efficient manner, and discuss their behavior regarding several quality criteria. The first application realm of these techniques is temporal reasoning.
Efficient Acceptable Design Exploration Based on Module Utility Selection
, 1999
"... In this paper, we present a design exploration framework, called WIZARD, which aims at finding module selections leading to acceptable designs while considering scheduling and resource binding under latency, and power constraints. The framework contains two phases: choosing the resource configuratio ..."
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Cited by 7 (5 self)
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In this paper, we present a design exploration framework, called WIZARD, which aims at finding module selections leading to acceptable designs while considering scheduling and resource binding under latency, and power constraints. The framework contains two phases: choosing the resource configuration, and determining a module binding for each resource. We introduce a powerful model, called acceptability function which models design objectives, based on tradeoffs among different design constraints as well as users' willingness of accepting a design. Module utility measure cooperating with inclusion scheduling is a key to the success of the method. The module utility reects the usefulness of the module based on the acceptability function. Inclusion scheduling is a basic tool to calculate the number of generic resources as well as determine module usefulness. A heuristic which perturbs module utility values based on the given acceptability function until they lead to superior selec...
Multidimensional Scaling of Fuzzy Dissimilarity Data
"... Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as distances between points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are expressed as intervals or fuzzy numbers. Each object is t ..."
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Cited by 7 (1 self)
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Multidimensional scaling is a wellknown technique for representing measurements of dissimilarity among objects as distances between points in a pdimensional space. In this paper, this method is extended to the case where dissimilarities are expressed as intervals or fuzzy numbers. Each object is then no longer represented by a point but by a crisp or a fuzzy region. To determine these regions, two algorithms are proposed and illustrated using typical datasets. Experiments demonstrate the ability of the methods to represent both the structure and the vagueness of dissimilarity measurements.
Towards Human Level Machine Intelligence, pp.1516 procedding of
 7th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering & Data Base (AIKED’08
"... many areasbut not in the realm of human level machine intelligence. Anyone who has been forced to use a dumb automated customer service system will readily agree. The Turing Test lies far beyond. Today, no machine can pass the Turing Test and none is likely to do so in the foreseeable future. Durin ..."
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Cited by 6 (0 self)
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many areasbut not in the realm of human level machine intelligence. Anyone who has been forced to use a dumb automated customer service system will readily agree. The Turing Test lies far beyond. Today, no machine can pass the Turing Test and none is likely to do so in the foreseeable future. During much of its early history, AI was rife with exaggerated expectations. A headline in an article published in the late forties of last century was headlined, ”Electric brain capable of translating foreign languages is being built. ” Today, more than half a century later, we do have translation software, but nothing that can approach the quality of human translation. Clearly, achievement of human level machine intelligence is a challenge that is hard to meet. Humans have many remarkable capabilities; there are two that stand out in importance. First, the capability to reason, converse and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, partiality of truth and possibility. And second, the capability to perform a wide variety of physical and mental tasks without any measurements and any computations. A prerequisite to achievement of
Investment using technical analysis and fuzzy logic
 FUZZY SETS AND SYSTEMS
, 2002
"... Deploy fuzzy logic engineering tools in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic s ..."
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Cited by 5 (0 self)
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Deploy fuzzy logic engineering tools in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. The only required inputs to these indicators are past sequence of stock prices. This method relies on fuzzy logic to formulate a decision making when certain price movements or certain price formations occur. The success of the system is measured by comparingsystem output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical
On the Implementation of Fuzzy Arithmetical Operations for Engineering Problems
 Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society  NAFIPS99
, 1999
"... Fuzzy arithmetic is a successful tool to solve engineering problems with uncertain parameters. The generalized mathematical operations for fuzzy numbers can theoretically be defined making use of Zadeh's extension principle. Practical realworld applications of fuzzy arithmetic, however, requir ..."
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Cited by 5 (2 self)
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Fuzzy arithmetic is a successful tool to solve engineering problems with uncertain parameters. The generalized mathematical operations for fuzzy numbers can theoretically be defined making use of Zadeh's extension principle. Practical realworld applications of fuzzy arithmetic, however, require an appropriate form of implementation for the fuzzy numbers and the fuzzy arithmetical operations. For this reason, the often applied concept of triangular fuzzy numbers and the more promising approach of discretized fuzzy numbers are presented and rated with respect to their practical application to solve engineering problems with uncertain parameters. As an example, a rather simple but typical problem of mechanical engineering is considered, consisting of determining the displacements in a twocomponent massless rod under tensile load with uncertain elasticity parameters. 1. Introduction To achieve reliable results for the numerical solution of engineering problems, exact values for the para...
A Nearly Strict Fuzzy Arithmetic for Solving Problems with Uncertainties
 In Proc. of the 19th International Conference of the North American Fuzzy Information Processing Society  NAFIPS 2000
, 2000
"... Fuzzy arithmetic is a powerful tool to solve engineering problems with uncertain parameters. In doing so, the uncertain parameters in the model equations are expressed by fuzzy numbers, and the problem is solved by using fuzzy arithmetic to carry out the mathematical operations in a generalized form ..."
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Cited by 5 (1 self)
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Fuzzy arithmetic is a powerful tool to solve engineering problems with uncertain parameters. In doing so, the uncertain parameters in the model equations are expressed by fuzzy numbers, and the problem is solved by using fuzzy arithmetic to carry out the mathematical operations in a generalized form. The practical use of standard fuzzy arithmetic, however, turns out to be very problematic, basically because of the overestimation effect which is responsible for a more or less large discrepancy between the proper arithmetical solution of the problem and the calculated one. In this paper, a new implementation of fuzzy arithmetic is presented by which those discrepancies in general can be reduced to a slight remainder and in many cases can even be totaly avoided. The effectiveness of the method is illustrated by some typical examples. 1. Introduction To achieve reliable results for the numerical solution of engineering problems, exact values for the parameters of the model equations shou...
Imprecise task schedule optimization
 In Proceedings of the International Conference on Fuzzy Systems
, 1997
"... Considerable research has been done in order to schedule tasks to a multiple processing systems. Some of the computation time of these tasks may, however, be imprecise due to the nature of the problem. In this paper, an imprecise task graph is used to model the problem where each node represents a t ..."
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Cited by 4 (0 self)
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Considerable research has been done in order to schedule tasks to a multiple processing systems. Some of the computation time of these tasks may, however, be imprecise due to the nature of the problem. In this paper, an imprecise task graph is used to model the problem where each node represents a task associated with its computation time. An algorithm, called rotation scheduling, is extended to handle the imprecise task scheduling and fuzzy arithmetic is used to estimate the size of a schedule. The goal of the algorithm is to give the optimized schedule as much as possible. The experiments showing the effectiveness of this approach are also presented.