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Requirements are investigated in this paper for each descriptor form subsystem, with which a causal/impulse free networked dynamic system (NDS) can be constructed. For this purpose, a matrix rank based necessary and sufficient condition is at first derived for the causality/impulse freeness of an NDS, in which the associated matrix depends affinely on subsystem connections. From this result, a necessary and sufficient condition is derived for each subsystem, such that there exists a subsystem connection matrix that leads to a causal/impulse free NDS. This condition further leads to a necessary and sufficient condition for the existence of a local static output feedback that guarantees the construction of a causal/impulse free NDS. A prominent property of these conditions are that all the involved numerical computations are performed independently on each individual subsystem, which is quite attractive in reducing computation costs and improving numerical stability for large scale NDS analysis and synthesis. Situations have also been clarified in which NDS causality/impulse freeness is independent of subsystem connections. It has also been made clear that under some situations, local static output feedbacks are not helpful in constructing a causal NDS.
This paper investigates requirements on a networked dynamic system (NDS) such that its subsystem interactions can be solely determined from experiment data or reconstructed from its overall model. The NDS is constituted from several subsystems whose
Requirements on subsystems have been made clear in this paper for a linear time invariant (LTI) networked dynamic system (NDS), under which subsystem interconnections can be estimated from external output measurements. In this NDS, subsystems may hav
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We study the problem of estimating the parameters (i.e., infection rate and recovery rate) governing the spread of epidemics in networks. Such parameters are typically estimated by measuring various characteristics (such as the number of infected and
Linear time-invariant (LTI) systems appear frequently in natural sciences and engineering contexts. Many LTI systems are described by ordinary differential equations (ODEs). For example, biological gene regulation, analog filter circuits, and simple