Kernel-based identification of positive systems
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Author / Producer
Date
2020
Publication Type
Conference Paper
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yes
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Abstract
In this paper, we introduce a novel method for identification of internally positive systems. In this regard, we consider a kernel-based regularization framework. For the existence of a positive realization of a given transfer function, necessary and sufficient conditions are introduced in the realization theory of the positive systems. Utilizing these conditions, we formulate a convex optimization problem by which we can derive a positive system for a given set of input-output data. The optimization problem is initially introduced in reproducing kernel Hilbert spaces where stable kernels are used for estimating the impulse response of system. Following that, employing theory of optimization in function spaces as well as the well-known representer theorem, an equivalent convex optimization problem is derived in finite dimensional Euclidean spaces which makes it suitable for numerical simulation and practical implementation. Finally, we have numerically verified the method by means of an example and a Monte Carlo analysis.
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published
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Book title
2019 IEEE 58th Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
1740 - 1745
Publisher
IEEE
Event
58th IEEE Conference on Decision and Control (CDC 2019)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
Notes
Conference lecture on December 11, 2019.