Catálogo de publicaciones - libros

This paper considers the task of constructing fuzzy prototypes for numerical data in order to characterize the data subgroups obtained after a clustering step. The proposed solution is motivated by the will of describing prototypes with a richer representation than point-based methods, and also to provide a characterization of the groups that catches not only the common features of the data pertaining to a group, but also their specificity. It transposes a method that has been designed for fuzzy data to numerical data, based on a prior computation of typicality degrees that are defined according to concepts used in cognitive science and psychology. The paper discusses the construction of prototypes and how their desirable semantics and properties can guide the selection of the various operators involved in the construction process.

Recently, in many of his in.uential and stimulating talks, Zadeh has been advocating the concept of a protoform, which stands for a prototypical form, as a crucial tool for the formalization of human consistent reasoning, deduction capabilities of search engines, etc.

In this study, a fuzzy logic system developed to control fluid balance during RT by changing in blood pressure (BP), heart rate (HR) and CVP value. Currently, the administration of intravascular volume is carried out primarily based on the experience and expertise of the anesthesiologist as there is no analytical method available to estimate the transplant patient’s fluid level. The development of a control system to assist the anesthesiologist, so that he or she can devote attention to other tasks that can’t yet be adequately automated during RT. To increase kidney viability in transplant surgery, patient safety and comfort during RT is one of the most important potential benefits of the system. For this project, 30 kidney transplantation operations, performed in Akdeniz University Organ Transplantation Center were completely followed and all physiological data recorded during last year. This data base is used for contribution of the fuzzy system membership functions and determine to base variable intervals. Also the developed system was tested with these operations records.

Some difficulties emerging during the construction of fuzzy behaviour-based control structures are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of fetching fuzzy rules directly from expert knowledge e.g. for the behaviour coordination module, the way of building a complete rule base is not always straightforward. One simple solution for overcoming the necessity of the complete rule base is the application of interpolation-based fuzzy reasoning methods, since interpolation-based fuzzy reasoning methods can serve usable (interpolated) conclusion even if none of the existing rules is hit by the observation. These methods can save the expert from dealing with derivable rules and help to concentrate on cardinal actions only. For demonstrating the applicability of the interpolation-based fuzzy reasoning methods in behaviour-based control structures a simple interpolation-based fuzzy reasoning method and its adaptation for behaviour-based control will be introduced briefly in this paper.

In this contribution we propose a new guidance law based on fuzzy logic that can be successfully applied to modeling and generating complicated offensive maneuver in an air combat between two aircraft. Based on human expert’s decision-making process, an intelligent based method is proposed to model the maneuvering. Fuzzy “if ... then...” rules are used to represent the pursuer preferences in guiding his/her system. The rules are directly obtained from expert’s knowledge. Each rule relates the desired moving directions of the pursuer to the task parameters. The control parameters of the aircraft are computed through a mean square error scheme. A large amount of simulations are used to approve the satisfactory performance of the model. The results show human like maneuvers can be generated by the proposed model.

A fuzzy approximation method called fuzzy transforms for approximation of continuous function is presented in this paper. It is shown how can be fuzzy transforms naturally generalized for functions with more variables. A fuzzy function as an approximated mapping is considered. This leads to an extension of fuzzy transforms for fuzzy function as well as to an extension of generalized fuzzy transforms for fuzzy functions with more variables. It is shown how the proposed method can be used as so called learning to obtain a fuzzy rule base for fuzzy control.

The general mathematical problem of fuzzy control is an interpolation problem: a list of fuzzy input-output data, usually provided by a list of linguistic control rules, should be realized as argument-value pairs for a suitably chosen fuzzy function. However, contrary to the usual understanding of interpolation, in the actual approaches this interpolation problem is considered as a global one: one uniformly and globally defined function should realize all the fuzzy input-output data. In standard classes of functions thus this interpolation problem often becomes unsolvable. Hence it becomes intertwined with an approximation problem which allows that the given fuzzy input-output data are realized only approximately by argument-value pairs. In this context the paper discusses some quite general sufficient conditions for the true solution of the interpolation problem, as well as similar conditions for suitably modified data, i.e. for a quite controlled approximation.

Galois connections appear in several areas of mathematics and computer science, and their applications. A Galois connection between sets X and Y is a pair 〈 ↑, ↓〉 of mappings ↑ assigning subcollections of Y to subcollections of X , and ↓ assigning subcollections of X to subcollections of Y . By definition, Galois connections have to satisfy certain conditions. Galois connections can be interpreted in the following manner: For subcollections A and B of X and Y , respectively, A ↑ is the collection of all elements of Y which are in a certain relationship to all elements from A , and B ↓ is the collection of all elements of X which are in the relationship to all elements in B . From the very many examples of Galois connections in mathematics, let us recall the following. Let X be the set of all logical formulas of a given language, Y be the set of all structures (interpretations) of the same language. For A ⊆ X and B ⊆ Y , let A ↑ consist of all structures in which each formula from A is true, let B ↓ denote the set of all formulas which are true in each structure from B . Then, ↑ and ↓ is a Galois connection.

The technique of fuzzy transform (F-transform for short) has been introduced in [ 6 , 5 ]. It consists of two phases: direct and inverse. We have proved that the inverse F-transform has good approximation properties and is very simple to use.

Nowadays, it is not necessary to advocate in favor of systems of fuzzy IFTHEN rules, because they are widely used in applications of fuzzy set theory such that fuzzy control, identification of dynamic systems, prediction of dynamic systems, decision-making, etc. The reason is in the fact that these systems can be effectively used as an instrument for representation of continuous dependencies. Therefore, the continuity property of a model of fuzzy IF-THEN rules is expected.