Catálogo de publicaciones - libros

The evaluation is a process that analyzes elements to achieve different objectives such as quality inspection, design, marketing exploitation and other fields in industrial companies. In many of these fields the items, products, designs, etc., are evaluated according to the knowledge acquired via human senses (sight, taste, touch, smell and hearing), in such cases, the process is called . In this type of evaluation process, an important problem arises as it is the modelling and management of uncertain knowledge, because the information acquired by our senses throughout human perceptions involves uncertainty, vagueness and imprecision.

The sensory evaluation of Olive oil plays a relevant role for the quality and properties of the commercialized product. In this contribution, we shall present a new evaluation model for Olive oil sensory evaluation based on a decision analysis scheme that will use the Fuzzy Linguistic Approach to facilitate the modelling and managing of the uncertainty and vagueness of the information acquired through the human perceptions in the sensory evaluation process.

We propose to adapt an interval-based index structure, Relational Interval trees, to support the process of determining the structure of an unknown chemical compound. Important information for retrieving relevant substructures that make up a compound can only be described in an imprecise way, resulting in interval-based values specifying chemical shifts. The access method was implemented on top of a commercial database system and evaluated experimentally. The results of these experiments show that Relational Interval trees are an efficient way of indexing data needed for structure elucidation.

RoboCupSoccer domain has several leagues which varies in the rule of play such as specification of players, number of players, field size, and time length. Nevertheless, each RoboCup league is a variant of a soccer league and therefore they are based on some basic rules of soccer. A layered design of agents system presented in [1] shows a modular approach to build control for a team of robots participating in RoboCupSoccer E-League. Based on this design, we propose a generalized architecture offering flexibility to switch between leagues and programming language while maintaining Prolog as cognitive layer. Prolog is a perfect tool to design strategies for soccer players using simple rules close to human reasoning. Sometimes this reasoning needs to deal with uncertainty, fuzziness or incompleteness of the information. In these cases it is useful Fuzzy Prolog [11,8,10,9]. In this paper we propose to use a combination of Prolog (that is crisp) and Fuzzy Prolog to implement the cognitive layer in RoboCupSoccer, which has the advantage of incorporating as conventional logic as fuzzy logic in this layer. A prototype of a team based on this architecture has been build for RoboCup Soccer Simulator, and we show that this approach provides a convenient way of incorporating a team strategy in high level (human-like) manner, where technical details are encapsulated and fuzzy information is represented.

The paper presents an approach to description of fuzzy discrete functions and Fourier transform of such functions taking in consideration their uncertainty. Conventional approach to uncertainty employs probabilistic description. Here, fuzzy logic theory is applied to describe this uncertainty. A definition of transform, called later Discrete Fuzzy Fourier Transform and definition of Inverse Discrete Fuzzy Fourier Transform are proposed. Some properties of such transformations and examples of applications and comparison with conventional approach are shown.

In this paper a general approach to combinatorial optimization problems with fuzzy weights is discussed. The results, valid for the interval-valued problems, are extended to the fuzzy-valued ones by exploiting the very recent notion of a gradual number. Some methods for determining the exact degrees of possible and necessary optimality and the possibility distributions of deviations of solutions and elements are proposed. The introduced notions are illustrated by practical examples.

The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.

Generally, as for Genetic Algorithms (GAs), it is not always optimal search efficiency, because genetic parameters (crossover rate, mutation rate and so on) are fixed. For this problem, we have already proposed Fuzzy Adaptive Search Method for GA (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. On the other hand, in order to improve the solution quality of GA, Parallel Genetic Algorithm (PGA) based on the local evolution in plural sub-populations (islands) and the migration of individuals between islands has been researched.

In this research, Fuzzy Adaptive Search method for Parallel GA (FASPGA) combined FASGA with PGA is proposed. Moreover as the improvement method for FASPGA, Diversity Measure based Fuzzy Adaptive Search method for Parallel GA (DM-FASPGA) is also proposed. Computer simulation was carried out to confirm the efficiency of the proposed method and the simulation results are also reported in this paper.

This paper deals with the design of a fuzzy (FC) based on pole assignment method for the control of a robotic system. The main idea is to design a supervisory fuzzy controller capable to adjust the controller parameters in order to obtain the desired axes positions under variationes of the robot parameters and payload variations. The behaviour of the closed loop fuzzy system controlled by the fuzzy controller is identical to the linear system whose state transition matrix is the desired one.

The main objective of this paper is the use of the affine Takagi-Sugeno (T-S) fuzzy model to represent both the controlled system and the fuzzy controller taking into consideration the effect of the constant term [1]. Most of the research works analyzed the T-S model assuming that the non-linear system is linearized with respect to the origin in each IF-THEN rule, which means that the consequent part of each rule is a linear function with zero constant term. This will in turn reduce the accuracy of approximating non-linear systems.

In this paper we introduce a collaboration framework for hyperheuristics to solve hard strip packing problems. We have designed a genetic based hyperheuristic to cooperate with a hill-climbing based hyperheuristic. Both of them use the most recently proposed low-level heuristics in the literature. REVAC, which has recently been proposed for tuning [18], has been used to find the best operators parameter values. The results obtained are very encouraging and have improved the results from both the single heuristics and the single hyperheuristics’ tests. Thus, we conclude that the collaboration among hyperheuristics is a good way to solve hard strip packing problems.

A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included.