Fuzzification of Boolean Networks

Dobrić, Vladimir

doi:10.1142/9789814619998_0047

Cited by 1 publication

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“…In this case, logical state variables and logical functions are realized as real-valued. This provides the drastic increases of the descriptive power of the model [36][37][38]. The behavior of a system can be quantified with much more precision, and thus, deeper insight into the system's dynamics is possible.…”

Section: Real-valued Realization Of Boolean Networkmentioning

confidence: 99%

Interpolative Boolean Networks

et al. 2017

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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.

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Section: Real-valued Realization Of Boolean Networkmentioning

confidence: 99%