摘要
为了更好地恢复ECG数据压缩中的原始信号,采用粒子群优化算法来求解ECG数据压缩中的最小误差问题.粒子群优化算法是基于一群粒子的智能运动而产生的随机进化计算方法.首先介绍了粒子群算法的原理和流程,其次结合拉格朗日函数和编码模型得出适应度函数,并将这种方法应用于ECG数据的压缩上,最后给出了PSO算法在ECG数据压缩上的应用实例,通过与SPIHT算法比较,文中算法的误差和平均PRD值都比SPIHT算法小.验证了粒子群算法在ECG数据压缩求误差极小值上的有效性,表明该算法具有广泛的应用前景.
In order to restore the original signal in ECG data compression better, a new and efficient swarm intelligence search method based on particle swarm optimization algorithm for solving the Problem of smallest error in ECG data compression is presented. Particle swarm optimization （PSO）, which is very easy to understand and implement, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarm. This paper first introduces a conceptual overview and detailed explanation of the PSO algorithm, and gives the fitness function with combination of Lagrange function and coding model, as well as the application for ECG compression, then presents several results optimized by PSO. Experimental results show the effectiveness of particle swarm optimization algorithm for solving the minimum error proplem in the ECG compression.
出处
《西安工业大学学报》
CAS
2010年第2期187-190,共4页
Journal of Xi’an Technological University
关键词
ECG压缩
粒子群优化算法
惯性权重
适应度函数
学习因子
electrocardiograph compression
PSO algorithm
inertia weight
fitness function
learning factor
