Search-Enhanced Instantaneous Frequency Detection Algorithm: A Preliminary Design
Phen Chiak See, Marta Molinas

TL;DR
This paper introduces SEIFD, a novel algorithm that employs genetic algorithms to accurately detect sinusoidal components in complex, nonlinear, non-stationary time-series data, enhancing frequency analysis.
Contribution
The paper proposes a new search-enhanced method using genetic algorithms for instantaneous frequency detection in challenging time-series data.
Findings
Successfully identifies start-time, end-time, frequency, and phase of sinusoidal components.
Demonstrates effectiveness in nonlinear, non-stationary data environments.
Provides a preliminary design for the SEIFD algorithm.
Abstract
This paper presents a method developed for finding sinusoidal components within a nonlinear non-stationary time-series data using Genetic Algorithm (GA) (a global optimization technique). It is called Search-Enhanced Instantaneous Frequency Detection (SEIFD) algorithm. The GA adaptively define the configuration of the components by simulating the solution finding process as a series of genetic evolutions. The start-time, end-time, frequency, and phase of each of these components are identified once convergence in the implementation is achieved.
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Iterative Learning Control Systems · Metaheuristic Optimization Algorithms Research
