Graph-Based Function Perturbation Analysis for Observability of Multivalued Logical Networkss.

Wang, Shuling; Li, Haitao · IEEE Trans Neural Netw Learn Syst · 2021

basic_science · Level V

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Abstract

Observability is a fundamental concept for the synthesis of both linear systems and nonlinear systems. This article devotes to discussing the robustness of observability for multivalued logical networks (MVLNs) subject to function perturbation and establishing a graph-based framework. First, based on the transition graph of undistinguishable pairs of states, a new graph-based criterion is presented for the observability of MVLNs. Second, a candidate set consisting of all suspicious undistinguishable pairs of states is defined, based on the cardinality of which and the graph-based condition, a series of effective criteria are proposed for the robustness of observability subject to function perturbations. Finally, the obtained results are applied to the robust observability analysis of the p53-MDM2 negative feedback regulatory loop.