The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation on this approach, the loss of information. It appears due to its information representation model (discrete terms) and the computational methods used when fusion and combination processes are performed on linguistic variables. In this contribution we propose a new fuzzy linguistic representation model based on the concept of "Symbolic Translation" for dealing with linguistic information in a continuous domain. Together with this representation model we shall develop a computational technique for fusing linguistic variables without loss of information. Keywords: Linguistic variables, linguistic modeling, fusion of linguistic information. 1 Introduction The problems depending on their aspects can deal with dierent types of information. Usually, the problems present quantitative aspects that can be assessed by means of precise numerical values, but in other cases the problems p...

Computing, in its usual sense, is centered on manipulation of numbers and symbols. In contrast, computing with words, or CW for short, is a methodology in which the objects of computation are words and propositions drawn from a natural language, e.g., small, large, far, heavy, not very likely, the price of gas is low and declining, Berkeley is near San Francisco, it is very unlikely that there will be a significant increase in the price of oil in the near future, etc. Computing with words is inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples of such tasks are parking a car, driving in heavy traffic, playing golf, riding a bicycle, understanding speech and summarizing a story. Underlying this remarkable capability is the brain’s crucial ability to manipulate perceptions – perceptions of distance, size, weight, color, speed, time, direction, force, number, truth, likelihood and other characteristics of physical and mental objects. Manipulation of perceptions plays a key role in human recognition, decision and execution processes. As a methodology, computing with words provides a foundation for a computational theory of perceptions – a theory which may have an important bearing on how humans make – and machines might make – perception-based rational decisions in an environment of imprecision, uncertainty and partial truth. A basic difference between perceptions and measurements is that, in general, measurements are crisp whereas perceptions are fuzzy. One of the fundamental aims of science has been and continues to be that of progressing from perceptions to measurements. Pursuit of this aim has led to brilliant successes. We have sent men to the moon; we can build computers

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Abstract—The aim of this paper is to model the processes of the aggregation of weighted information in a linguistic framework. Three aggregation operators of weighted linguistic information are presented: linguistic weighted disjunction (LWD) operator, linguistic weighted conjunction (LWC) operator, and linguistic weighted averaging (LWA) operator. A study of their axiomatics is presented to demonstrate their rational aggregation. Index Terms — Aggregation operators, fuzzy linguistic quantifier, linguistic modeling. I.

A framework for the qualitative representation of positional information in a two-dimensional space is presented. Qualitative representations use discrete quantity spaces, where a particular distinction is introduced only if it is relevant to the context being modeled. This allows us to build a flexible framework that accommodates various levels of granularity and scales of reasoning. Knowledge about position in large-scale space is commonly represented by a combination of orientation and distance relations, which we express in a particular frame of reference between a primary object and a reference object. While the representation of orientation comes out to be more straightforward, the model for distances requires that qualitative distance symbols be mapped to geometric intervals in order to be compared; this is done by defining structure relations that are able to handle, among others, order of magnitude relations; the frame of reference with its three components (distance system, s...

In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instanciations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instanciations are provided owing to the relaxation of ...

It is a deep-seated tradition in science to view uncertainty as a province of probability theory. The generalized theory of uncertainty (GTU) which is outlined in this paper breaks with this tradition and views uncertainty in a much broader perspective. Uncertainty is an attribute of information. A fundamental premise of GTU is that information, whatever its form, may be represented as what is called a generalized constraint. The concept of a generalized constraint is the centerpiece of GTU. In GTU, a probabilistic constraint is viewed as a special––albeit important––instance of a generalized constraint. A generalized constraint is a constraint of the form X isr R, where X is the constrained variable, R is a constraining relation, generally non-bivalent, and r is an indexing variable which identifies the modality of the constraint, that is, its semantics. The

This paper presents a consensus model in group decision making under linguistic assessments. It is based on the use of linguistic preferences to provide individuals' opinions, and on the use of fuzzy majority of consensus, represented by means of a linguistic quantifier. Several linguistic consensus degrees and linguistic distances are defined, acting on three levels. The consensus degrees indicate how far a group of individuals is from the maximum consensus, and linguistic distances indicate how far each individual is from current consensus labels over the preferences. This consensus model allows to incorporate more human consistency in decision support systems.

A model for the identification of speech sounds is proposed that assumes that (a) the acoustic cues are perceived independently, (b) feature evaluation provides information about the degree to which each quality is present in the speech sound, (c) each speech sound is denned by a propositional prototype in longterm memory that determines how the featural information is integrated, and (d) the speech sound is identified on the basis of the relative degree to which it matches the various alternative prototypes. The model was supported by the results of an experiment in which subjects identified stop-consonant-vowel syllables that were factorially generated by independently varying acoustic cues for voicing and for place of articulation. This experiment also replicated previous findings of changes in the identification boundary of one acoustic dimension as a function of the level of another dimension. These results have previously been interpreted as evidence for the interaction of the perceptions of the acoustic features themselves. In contrast, the present model provides a good description of the data, including these boundary changes, while still maintaining complete

In this paper, a sequential selection process in group decision making under linguistic assessments is presented, where a set of linguistic preference relations represents individuals preferences. A collective linguistic preference is obtained by means of a defined linguistic ordered weighted averaging operator whose weights are chosen according to the concept of fuzzy majority, specified by a fuzzy linguistic quantifier. Then we define the concepts of linguistic nondora nance, linguistic dominance, and strict dominance degrees as parts of the sequential selection process. The solution alternative(s) is obtained by applying these concepts.