Displaying similar documents to “Fuzzy set theory for cumulative trauma prediction.”

Some practical problems in fuzzy sets-based decision support systems.

Alejandro Sancho-Royo, José Luis Verdegay, Edmundo Vergara-Moreno (1999)

Mathware and Soft Computing

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In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the question, first, of the practical determination of membership functions, second of the management of the fuzziness in some optimisation models, and finally of using fuzzy rules for terminating conventional...

Fuzzy termination criteria in Knapsack Problem algorithms.

José Luis Verdegay, Edmundo Vergara-Moreno (2000)

Mathware and Soft Computing

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Fuzzy rule based termination criteria are introduced in two conventional and exact algorithms solving Knapsack Problems. As a consequence two new solution algorithms are obtained. These algorithms are heuristic ones with a high performance. The efficiency of the algorithms obtained is illustrated by solving some numerical examples.

Spectral fuzzy classification system: a supervised approach.

Ana Del Amo, Daniel Gómez, Javier Montero (2003)

Mathware and Soft Computing

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The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy...

Improvement to the cooperative rules methodology by using the ant colony system algorithm.

Rafael Alcalá, Jorge Casillas, Oscar Cordón, Francisco Herrera (2001)

Mathware and Soft Computing

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The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems. Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one....

Refinement of a fuzzy control rule set.

Antonio González, Raúl Pérez (1998)

Mathware and Soft Computing

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Fuzzy logic controller performance depends on the fuzzy control rule set. This set can be obtained either by an expert or from a learning algorithm through a set of examples. Recently, we have developed SLAVE an inductive learning algorithm capable of identifying fuzzy systems. The refinement of the rules proposed by SLAVE (or by an expert) can be very important in order to improve the accuracy of the model and in order to simplify the description of the system. The refinement algorithm...

Application of fuzzy techniques to the design of algorithms in computer vision.

Eduard Montseny, Pilar Sobrevilla (1998)

Mathware and Soft Computing

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In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the analysis rules, a fuzzy set which will be obtained by applying an aggregation function which will...

A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules

Jacek Łęski, Tomasz Czogała (2005)

International Journal of Applied Mathematics and Computer Science

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First, a fuzzy system based on ifFirst, a fuzzy system based on if-then rules and with parametric consequences is recalled. Then, it is shown that the globalthen rules and with parametric consequences is recalled. Then, it is shown that the global and local ε-insensitive learning of the above fuzzy system may be presented as a combination of both an ε-insensitive gradient method and solving a system of linear inequalities. Examples are given of using the introduced method to design fuzzy...

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns...