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Kecskemet, Hungary

Kovacs T.,Kecskemet College
Digital Signal Processing: A Review Journal | Year: 2012

In the present paper a new method is proposed for separating the individual periodic components of a mixed signal. The method is capable to extract not only a harmonic but an anharmonic signal component from the mixture. To achieve this, the component is extracted by an FIR narrowband filter, which can modulate the output harmonic signal by an appropriate time-shift function. The search for this function is based on the minimization of a functional, which is calculated as the sum of the unsigned differences of the separated signal in a certain time window. The theoretical basis of this optimization method is that the functional above has global minimum if the separation is complete. The introduced tests show that the proposed method is more robust than the matrix algebraic separation (MAS) system in the case of a slightly frequency-modulated test signal. © 2012 Elsevier Inc. All rights reserved.

Johanyak Z.C.,Kecskemet College
Computing and Informatics | Year: 2013

Melt volume-flow rate (MVR) is one of the most important quality indicators of composite materials, which depends on the proportion of the component materials. This paper reports the development of a low complexity fuzzy model that describes the relation between percentage amount of multiwall carbon nanotube (MWCNT), acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC) and MVR of the resulting composite. The rule base was generated from a sample data set obtained from experiments by the rule base extension using default set shapes (RBE-DSS) method, and the applied fuzzy inference technique was the least squares method based fuzzy rule interpolation (LESFRI). The resulting model was validated against a separate test data set as well, and it was compared to a fuzzy model generated by a traditional commercial software tool.

Johanyak Z.C.,Kecskemet College
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics | Year: 2010

Fuzzy rule interpolation methods make possible the development of fuzzy rule based systems applying a low complexity and compact rule base that contains only the most relevant rules. They are able to infer even in those regions of the antecedent space where there are no applicable rules. In this paper, we present a novel method called Fuzzy Rule Interpolation based on Subsethood Values (FRISUV) that calculates the conclusion directly from the observation and the known rules with a low complexity algorithm. ©2010 IEEE.

Fabian C.I.,Kecskemet College | Fabian C.I.,Eotvos Lorand University | Mitra G.,Brunel University | Roman D.,Brunel University
Mathematical Programming | Year: 2011

Second-order stochastic dominance (SSD) is widely recognised as an important decision criterion in portfolio selection. Unfortunately, stochastic dominance models are known to be very demanding from a computational point of view. In this paper we consider two classes of models which use SSD as a choice criterion. The first, proposed by Dentcheva and Ruszczyński (J Bank Finance 30:433-451, 2006), uses a SSD constraint, which can be expressed as integrated chance constraints (ICCs). The second, proposed by Roman et al. (Math Program, Ser B 108:541-569, 2006) uses SSD through a multi-objective formulation with CVaR objectives. Cutting plane representations and algorithms were proposed by Klein Haneveld and Van der Vlerk (Comput Manage Sci 3:245-269, 2006) for ICCs, and by Künzi-Bay and Mayer (Comput Manage Sci 3:3-27, 2006) for CVaR minimization. These concepts are taken into consideration to propose representations and solution methods for the above class of SSD based models. We describe a cutting plane based solution algorithm and outline implementation details. A computational study is presented, which demonstrates the effectiveness and the scale-up properties of the solution algorithm, as applied to the SSD model of Roman et al. (Math Program, Ser B 108:541-569, 2006). © 2009 Springer and Mathematical Programming Society.

Johanyak Z.C.,Kecskemet College
12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011 - Proceedings | Year: 2011

Fuzzy rule interpolation (FRI) methods could be advantageous tools for fuzzy inference owing to their capability to reason in sparse rule bases as well. The fuzzy rule interpolation by the least squares method (LESFRI) like most of the available techniques is designed so that it can be implemented easily only by using the traditional structure based fuzzy inference system (FIS) representation also applied by the FRI Matlab ToolBox. In this paper, we propose a new vector based internal FIS representation (VFIS) and an enhanced version of LESFRI (VLESFRI) designed for the use of VFIS. The performance improvement achievable by the application of VLESFRI is proved by several test results. © 2011 IEEE.

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