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1 Journal of Telecommunications and Information Technology

1 2024

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Machine Learning Based System Identification with Binary Output Data Using Kernel Methods

Fateh Rachid, Oualla Hicham, Azougaghe Es-said, Darif Anouar, Boumezzough Ahmed, Safi Said, Pouliquen Mathieu, Frikel Miloud

Journal of Telecommunications and Information Technology

2024

nr 1

17--25

Within the realm of machine learning, kernel meth-ods stand out as a prominent class of algorithms with widespreadapplications, including but not limited to classification, regres-sion, and identification tasks. Our paper addresses the chal-lenging problem of identifying the finite impulse response (FIR)of single-input single-output nonlinear systems under the in-fluence of perturbations and binary-valued measurements. Toovercome this challenge, we exploit two algorithms that leveragethe framework of reproducing kernel Hilbert spaces (RKHS) toaccurately identify the impulse response of the Proakis C chan-nel. Additionally, we introduce the application of these kernelmethods for estimating binary output data of nonlinear systems.We showcase the effectiveness of kernel adaptive filters in identi-fying nonlinear systems with binary output measurements, asdemonstrated through the experimental results presented in thisstudy.