arXiv subjects

Rajintha Gunawardena

Publications and source records attributed to Rajintha Gunawardena.

3 recordsLinked to original sources

arXiv2026

NL-FRA: A MATLAB package for nonlinear frequency response analysis

Nonlinear Output Frequency Response Functions (NOFRFs) provide a one-dimensional frequency-domain representation of nonlinear dynamics, enabling direct decomposition of an output spectrum into contributions from different orders of nonlinearity. NL-FRA (NonLinear Frequency Response Analysis) is an open-source MATLAB package implementing a data-driven Least Squares (LS) method for estimating NOFRFs directly from system input-output data. It provides an integrated workflow for NOFRF estimation, validation, and visualisation, together with nonlinear transmissibility analysis. The package supports different types of inputs, i.e. general band-limited, multi-one and harmonic inputs, and includes routines for identifying the frequency ranges over which each nonlinear order contributes to the output. Furthermore, since the LS approach estimates NOFRFs directly from input-output data, it can be applied to experimental measurements or data generated using any suitable dynamic model of the system. The software facilitates interpretable nonlinear frequency-domain analysis for applications including fault diagnosis, condition and structural health monitoring, and biomedical engineering.

eess.SY↗

arXiv2026

A Dynamical Systems and System Identification Framework for Phase Amplitude Coupling Analysis

Phase-amplitude coupling (PAC), a form of cross-frequency coupling, is involved in diverse cognitive functions and neural communication, making its accurate detection and characterisation essential yet challenging. Most existing methods infer PAC from variations in instantaneous phase and amplitude profiles, but are limited by sensitivity to filter bandwidths, inconsistent performance across noise levels and data lengths, and vulnerability to spurious couplings. Here, we formulate PAC as a nonlinear dynamical-systems phenomenon characterised by quadratic phase coupling (QPC), and propose a nonlinear system identification framework that directly models the dynamics generating PAC. Rather than relying on filtered phase and amplitude fluctuations, the proposed method identifies a generative nonlinear model, enabling noise-free simulation of the estimated dynamics and model-based characterisation of coupling strength and preferred phase. It also provides dynamically grounded criteria for identifying harmonic- and intermodulation-related false detections, remains robust under high noise, and yields consistent characterisation despite changes in slow-frequency power. In simulations and rat hippocampal local field potentials (LFP) recordings, the proposed method produced more sharply localised and frequency-specific PAC estimates than benchmark filtering-based methods, while preserving detailed preferred-phase information. In simulations, it rejected harmonic-related spurious coupling, remained robust at a signal-to-noise ratio (SNR) of 2 and reasonably robust at an SNR of 1, and reliably characterised PAC using 5-second analysis windows. These results establish nonlinear system identification as a complementary dynamical framework for detecting and characterising PAC, with particular advantages for noisy and short-duration neural recordings susceptible to spurious coupling.

q-bio.NC↗

arXiv2024

NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models

System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately reflect the underlying system's behaviour. This paper introduces NonSysId, an open-sourced MATLAB software package designed for nonlinear system identification, specifically focusing on NARMAX models. The software incorporates an advanced term selection methodology that prioritises on simulation (free-run) accuracy while preserving model parsimony. A key feature is the integration of iterative Orthogonal Forward Regression (iOFR) with Predicted Residual Sum of Squares (PRESS) statistic-based term selection, facilitating robust model generalisation without the need for a separate validation dataset. Furthermore, techniques for reducing computational overheads are implemented. These features make NonSysId particularly suitable for real-time applications such as structural health monitoring, fault diagnosis, and biomedical signal processing, where it is a challenge to capture the signals under consistent conditions, resulting in limited or no validation data.

eess.SY↗