College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
IEEE Trans Biomed Eng. 2012 Dec;59(12):3412-21. doi: 10.1109/TBME.2012.2216265. Epub 2012 Aug 30.
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.
一种部分扰动粒子群优化（PPSO）方法被提出，用于从动作电位数据中识别 Beeler-Reuter（BR）方程的参数。在 PPSO 算法中，63 个 BR 方程参数被分成若干组，通过组合这些组来形成参数模式。当搜索过程受到限制时，PPSO 通过使用这些模式部分扰动全局最优粒子的坐标来增强传统粒子群优化（CPSO）的能力。BR 方程和 Luo 和 Rudy（1991）方程模拟的心肌细胞的“实验数据”被用于测试 PPSO 的算法。测试结果表明，PPSO 能够有效地识别不同心肌细胞的 BR 方程参数，同时仍然保留 CPSO 的概念简单性和易于实现性。
