EuDML | Browse

Page 1

Displaying 1 – 6 of 6

GTES : une méthode de simulation par jeux et apprentissage pour l'analyse des systèmes d'acteurs

Y. Caseau (2009)

RAIRO - Operations Research

Cet article décrit une approche de la modélisation d'un système d'acteurs, particulièrement adaptée à la modélisation des entreprises, fondée sur la théorie des jeux [11] et sur l'optimisation par apprentissage du comportement de ces acteurs. Cette méthode repose sur la combinaison de trois techniques : la simulation par échantillonnage (Monte-Carlo), la théorie des jeux pour ce qui concerne la recherche d'équilibre entre les stratégies, et les méthodes heuristiques d'optimisation locale,...

Modified neural network algorithms for predicting trading signals of stock market indices.

Tilakaratne, C.D., Mammadov, M.A., Morris, S.A. (2009)

Journal of Applied Mathematics and Decision Sciences

Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison

Shonak Bansal, Neena Gupta, Arun Kumar Singh (2017)

Open Mathematics

Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR) sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results...

On the numerical resolution of Isaac's inequalities

Silvia C. Di Marco (1997)

RAIRO - Operations Research - Recherche Opérationnelle

Realization of the Chess Mate Solver Application

Vladan V. Vučković (2004)

The Yugoslav Journal of Operations Research

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

Alina Constantinescu (2007)

Applications of Mathematics

In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.