2005, Vol. XII, Núm. 1
http://hdl.handle.net/2099/1817
Thu, 30 Mar 2017 20:50:27 GMT2017-03-30T20:50:27ZA note on the symmetric difference in lattices
http://hdl.handle.net/2099/2056
A note on the symmetric difference in lattices
Renedo, Eloy; Trillas i Gay, Enric; Alsina Català, Claudi
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.
Fri, 27 Oct 2006 16:19:57 GMThttp://hdl.handle.net/2099/20562006-10-27T16:19:57ZRenedo, EloyTrillas i Gay, EnricAlsina Català, ClaudiThe 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.A fuzzy logic approach to assembly line
http://hdl.handle.net/2099/2055
A fuzzy logic approach to assembly line
Fonseca, Daniel J.; Elam, Matthew; Karr, Charles L.; Guest, C.L.
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.
Fri, 27 Oct 2006 16:18:45 GMThttp://hdl.handle.net/2099/20552006-10-27T16:18:45ZFonseca, Daniel J.Elam, MatthewKarr, Charles L.Guest, C.L.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.On the transfer principle in fuzzy theory
http://hdl.handle.net/2099/2054
On the transfer principle in fuzzy theory
Kondo, Michiro; Dudek, Wieslaw A.
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”.
Fri, 27 Oct 2006 16:17:56 GMThttp://hdl.handle.net/2099/20542006-10-27T16:17:56ZKondo, MichiroDudek, Wieslaw A.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”.A heuristic forecasting model for stock decision
http://hdl.handle.net/2099/2053
A heuristic forecasting model for stock decision
Zhang, D.; Jiang, Q.; Li, X.
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.
Fri, 27 Oct 2006 16:16:11 GMThttp://hdl.handle.net/2099/20532006-10-27T16:16:11ZZhang, D.Jiang, Q.Li, X.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.A multistrategy approach for digital text
http://hdl.handle.net/2099/2052
A multistrategy approach for digital text
Castillo, María Dolores del; Serrano Moreno, José Ignacio
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.
Fri, 27 Oct 2006 16:14:30 GMThttp://hdl.handle.net/2099/20522006-10-27T16:14:30ZCastillo, María Dolores delSerrano Moreno, José IgnacioThe 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.On the central limit theorem on IFS-events
http://hdl.handle.net/2099/2051
On the central limit theorem on IFS-events
Petrovicova, Jozefina; Beloslav, Riecan
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
Fri, 27 Oct 2006 15:53:19 GMThttp://hdl.handle.net/2099/20512006-10-27T15:53:19ZPetrovicova, JozefinaBeloslav, RiecanA 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