http://hdl.handle.net/2099/1817 2013-05-19T00:09:58Z http://hdl.handle.net/2099/2056 Title: A note on the symmetric difference in lattices Authors: Renedo, Eloy; Trillas i Gay, Enric; Alsina Català, Claudi Abstract: The paper introduces a definition of symmetric difference in lattices with negation, presents its general properties and studies those that are typical of ortholattices, orthomodular lattices, de Morgan and boolean algebras. 2005-01-01T00:00:00Z http://hdl.handle.net/2099/2055 Title: A fuzzy logic approach to assembly line Authors: Fonseca, Daniel J.; Elam, Matthew; Karr, Charles L.; Guest, C.L. Abstract: This paper deals with the use of fuzzy set theory as a viable alternative method for modelling and solving the stochastic assembly line balancing problem. Variability and uncertainty in the assembly line balancing problem has traditionally been modelled through the use of statistical distributions. This may not be feasible in cases where no historical data exists. Fuzzy set theory allows for the consideration of the ambiguity involved in assigning processing and cycle times and the uncertainty contained within such time variables. Two widely used line balancing methods, the COMSOAL and Ranked Positional Weighting Technique, were modified to solve the balancing problem with a fuzzy representation of the time variables. The paper shows that the new fuzzy methods are capable of producing solutions similar to, and in some cases better than, those reached by the traditional methods. 2005-01-01T00:00:00Z http://hdl.handle.net/2099/2054 Title: On the transfer principle in fuzzy theory Authors: Kondo, Michiro; Dudek, Wieslaw A. Abstract: We show in this paper that almost all results proved in many papers about fuzzy algebras can be proved uniformly and immediately by using so-called “Transfer Principle”. 2005-01-01T00:00:00Z http://hdl.handle.net/2099/2053 Title: A heuristic forecasting model for stock decision Authors: Zhang, D.; Jiang, Q.; Li, X. Abstract: This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting model was found capable of consistently outperforming this benchmark strategy. 2005-01-01T00:00:00Z http://hdl.handle.net/2099/2052 Title: A multistrategy approach for digital text Authors: Castillo, María Dolores del; Serrano Moreno, José Ignacio Abstract: The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular, flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain. 2005-01-01T00:00:00Z http://hdl.handle.net/2099/2051 Title: On the central limit theorem on IFS-events Authors: Petrovicova, Jozefina; Beloslav, Riecan Abstract: A probability theory on IFS-events has been constructed in [3], and axiomatically characterized in [4]. Here using a general system of axioms it is shown that any probability on IFS-events can be decomposed onto two probabilities on a Lukasiewicz tribe, hence some known results from [5], [6] can be used also for IFS-sets. As an application of the approach a variant of Central limit theorem is presented 2005-01-01T00:00:00Z