Parameter Identification of an Over-Determined and Over-Constrained System using PSO

This paper proposes a curve fitting method for an over-determined system by a function that is the sum of sinusoidal terms. A Particle Swarm Optimization (PSO) algorithm for this trigonometry curve fitting having a large number of unknown parameters is developed, which is needed in engineering appli...

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Bibliographic Details
Published in2018 3rd International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH) pp. 1 - 5
Main Authors Rao, L. Navinkumar, Gairola, Sanjay
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2018
Subjects
Convergence
Curve fitting
Data models
Mathematical model
Over-determined system
Parameter estimation
Particle swarm optimization
Particle Swarm Optimization (PSO)
Root Mean Square Error (RMSE)
Sine function
Software algorithms
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Summary:This paper proposes a curve fitting method for an over-determined system by a function that is the sum of sinusoidal terms. A Particle Swarm Optimization (PSO) algorithm for this trigonometry curve fitting having a large number of unknown parameters is developed, which is needed in engineering applications. For the over-determined system, PSO based algorithm employs an additional hybrid term for velocity updation to determine the unknown parameters of the sine function. This additional hybrid term provides a better convergence and the iterations proceeds systematically. The algorithm is validated by fitting curve with a function having 18 parameters of mixed type. The code for the algorithm in MATLAB software is presented to establish the general approach.
DOI:10.1109/CIPECH.2018.8724313