Paper Info
Title
Identification of the parameters of the Beeler-Reuter ionic equation with a partially perturbed particle swarm optimization.
Abstract
A partially perturbed particle swarm optimization (PPSO) has been proposed for identifying the parameters of the Beeler-Reuter (BR) equation from action potential data. In the PPSO algorithm, the 63 BR equation parameters are divided into groups, and parameter patterns are made from the combination of the groups. PPSO enhances the capability of conventional particle swarm optimization (CPSO) by partially perturbing the coordinates of the globally best particle with the patterns when the searching process is locally confined. "Experimental data" were produced for cardiac myocytes simulated by the BR equation and the equation of Luo and Rudy (1991), and were used to test the algorithm of PPSO. The test results show that PPSO was able to identify the parameters of the BR equation effectively for different cardiac myocytes, while still retaining the conceptual simplicity and easy implementation of CPSO.
Year
DOI
Venue
2012
10.1109/TBME.2012.2216265
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
conventional particle swarm optimization,cellular biophysics,br equation parameters,ppso algorithm,beeler–reuter (br) equation,particle swarm optimisation,cardiac myocytes,partially perturbed particle swarm optimization,beeler-reuter (br) equation,cardiac myocyte,parameter identification,particle swarm optimization (pso),muscle,beeler-reuter ionic equation,action potential data,particle swarm optimization,calcium,electric potential,mathematical model
Ionic bonding,Particle swarm optimization,Applied mathematics,Cellular biophysics,Control theory,Electronic engineering,Mathematics,Particle
Journal
Volume
Issue
ISSN
59
12
1558-2531
Citations 
PageRank 
References 
2
0.44
0
Authors
5
Name
Order
Citations
PageRank
Fulong Chen1126.51
Angdi Chu220.44
Xuefei Yang320.44
Yao Lei420.44
Jizheng Chu572.26