Abstract
The chapter is devoted to applications of selected methods of computational intelligence: evolutionary algorithms and artificial neural networks, in identification of physical systems being under the uncertain conditions. Uncertainties can occur in boundary conditions, in material coefficients or some geometrical parameters of systems and are modeled by three kinds of granularity: interval mathematics, fuzzy sets and theory of probability. In order to evaluate fitness functions the interval, fuzzy and stochastic finite element methods are applied to solve granular boundary-value problems for considered physical systems. Several numerical tests and examples of identification of uncertain parameters are presented.
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Burczyński, T., Orantek, P. (2009). Uncertain Identification Problems in the Context of Granular Computing. In: Bargiela, A., Pedrycz, W. (eds) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92916-1_14
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DOI: https://doi.org/10.1007/978-3-540-92916-1_14
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