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Abstract
In many practical applications it might be desirable to excite only point at a time in an interconnection of multiple dynamic subsystems (e.g. large-scale system). Therefore multiple experiments need to be combined to successfully identify one or more subsystems in the network of subsystems. This papers illustrates how the identification of a linear subsystem of a dynamical network containing one or more nonlinear subsystems can result in biased estimates when multiple experiments are combined using the Best Linear Approximation (BLA) based approach.
Highlights
Large scale mechanical systems consisting of many components, the electrical grid, biological systems or industrial plant can be interpreted as the interconnection of multiple subsystems, i.e. a dynamic network setting.The identification of linear dynamical networks has received quite some attention over the last years focusing on e.g. network structure detection [1, 2, 3], identification of one or more subsystems in the network [3, 4, 5, 6], input selection [7], and multiple noise frameworks [8, 6]
Multiple experiments need to be combined to successfully identify one or more subsystems in the network of subsystems. This papers illustrates how the identification of a linear subsystem of a dynamical network containing one or more nonlinear subsystems can result in biased estimates when multiple experiments are combined using the Best Linear Approximation (BLA) based approach
Multiple experiments need to be combined to successfully identify of one or more subsystems in the network. This papers illustrates how the identification of a linear subsystem of a dynamical network containing one or more nonlinear subsystems can result in biased estimates when multiple experiments are combined using the Best Linear Approximation (BLA) based approach presented in [12]
Summary
Large scale mechanical systems consisting of many components, the electrical grid, biological systems or industrial plant can be interpreted as the interconnection of multiple subsystems, i.e. a dynamic network setting. Multiple experiments need to be combined to successfully identify of one or more subsystems in the network This papers illustrates how the identification of a linear subsystem of a dynamical network containing one or more nonlinear subsystems can result in biased estimates when multiple experiments are combined using the Best Linear Approximation (BLA) based approach presented in [12]. A dynamic network (see Figure 1) consists of a total of L nodes, representing internal variables of the network, which are interconnected with other nodes by (nonlinear) dynamic systems. A node signal, denoted wi(t), is obtained as the sum of the outputs of the incoming (nonlinear).
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