Rong Zhao , Jun-e Feng , Qingchun Meng , Biao Wang
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Identification of a class of singular Boolean control networks
System identification, recognized as an inverse control problem, is a significant aspect of modern control theory. This study focuses on addressing the identification problem related to a specific category of singular Boolean networks (SBNs) and singular Boolean control networks (SBCNs). The introduction of two novel concepts, namely the admissibility and solvability matrices, enables the establishment of conditions for determining the existence and uniqueness of solutions for SBNs and SBCNs. Then criteria are deduced to identify the number of dynamic equations. Based on observability, controllability and detectability, several conditions are presented to characterize identification. Among them, two crucial results show: When the solution to an SBN or SBCN is unique, the SBN is identifiable if and only if it is observable, and the SBCN is identifiable if and only if it is O1-observable, which is the most general type of observability. Besides, effective algorithms are devised to implement identification. Furthermore, the study delves into the normalization issue using the admissibility matrix, which provides a possibility to reduce the identified SBN or SBCN to a lower-order BN or BCN.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.