Interpolative Boolean Networks

Autor: Ana Poledica, Aleksandar Rakićević, Vladimir Dobrić, Bratislav Petrović, Pavle Milošević
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Complexity, Vol 2017 (2017)
ISSN: 1076-2787
DOI: 10.1155/2017/2647164
Popis: Boolean networks are used for modeling and analysis of complex systems of interacting entities. Classical Boolean networks are binary and they are relevant for modeling systems with complex switch-like causal interactions. More descriptive power can be provided by the introduction of gradation in this model. If this is accomplished by using conventional fuzzy logics, the generalized model cannot secure the Boolean frame. Consequently, the validity of the model’s dynamics is not secured. The aim of this paper is to present the Boolean consistent generalization of Boolean networks, interpolative Boolean networks. The generalization is based on interpolative Boolean algebra, the [0,1]-valued realization of Boolean algebra. The proposed model is adaptive with respect to the nature of input variables and it offers greater descriptive power as compared with traditional models. For illustrative purposes, IBN is compared to the models based on existing real-valued approaches. Due to the complexity of the most systems to be analyzed and the characteristics of interpolative Boolean algebra, the software support is developed to provide graphical and numerical tools for complex system modeling and analysis.
Databáze: OpenAIRE
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