External Identification of a Reciprocal Lossy Multiport Circuit under Measurement Uncertainties by Riemannian Gradient Descent

Simone Fiori () and Jing Wang
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Simone Fiori: Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche (UPM), Via Brecce Bianche, I-60131 Ancona, Italy
Jing Wang: School of Information, Beijing Wuzi University, Fuhe Street, Tongzhou District, Beijing 101149, China

Energies, 2023, vol. 16, issue 6, 1-31

Abstract: The present paper deals with the external identification of a reciprocal, special passive, 2 n -port network under measurement uncertainties. In the present context, the multiport model is represented by an admittance matrix and the condition that the network is ‘reciprocal special passive’ refers to the assumption that the real part of the admittance matrix is symmetric and positive-definite. The key point is to reformulate the identification problem as a matrix optimization program over the matrix manifold S + ( 2 n ) × S ( 2 n ) . The optimization problem requires a least-squares criterion function designed to cope with over-determinacy due to the incoherent data pairs whose cardinality exceeds the problem’s number of degrees of freedom. The present paper also proposes a numerical solution to such an optimization problem based on the Riemannian-gradient steepest descent method. The numerical results show that the proposed method is effective as long as reasonable measurement error levels and problem sizes are being dealt with.

Keywords: system identification; multiport model; Riemannian manifold; gradient-steepest-descent optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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