Luis Aguirre | Searcharxiv

arXiv2023

Detecting Phase Synchronization in Latent Variable Subspace: Non-generating Partitions and Symbol Sequence Statistics

The detection of phase synchronization of coupled chaotic oscillators which are not phase-coherent is known to be a challenging task. In this work a method to detect and measure phase synchronization is presented. The procedure uses symbol sequence statistics together with Principal Component Analysis (PCA) and is applied in the phase synchronization analysis of pairs of coupled chaotic systems with different characteristics. Using PCA, we extract a 3D space (called latent space) from the original 6D space of the coupled oscillators. When the oscillators are in complete synchronization, the latent space represents the dynamics of an isolated oscillator. However, as synchronization deteriorates, the latent space becomes increasingly disorganized, although it does retain some level of organization during phase synchronization. A 2D Poincaré-type section is defined in the latent space and the corresponding 1D map is used to define a non-generating partition such that an arbitrary symbol sequence is forbidden for any synchronized regime. It is shown that the probability of occurrence of such a symbol sequence is closely related to the quality of phase synchronization. The procedure does not require a phase definition or complicated partitioning algorithms, which is performed by a simple threshold-crossing technique. This method requires data from different levels of synchronization to be able to determine the required non-generating partition.

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arXiv2020

Multi-objective Evolutionary Approach to Grey-Box Identification of Buck Converter

The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.

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