PSO并行优化LSSVR非线性黑箱模型辨识

针对非线性黑箱系统辨识中存在不确定性、高阶次,采用常规辨识方法建立其精确数学模型十分困难等问题,提出一种基于自适应粒子群算法的最小二乘支持向量机回归(PSO-LSSVR)非线性系统辨识方法.该方法采用2组自适应粒子群算法并行计算模型,分别利用自适应粒子群算法对LSSVR中的参数进行自动选取和矩阵迭代求解,既克服了传统LSSVR参数难以确定的缺点,提高了辨识精度,同时避免了复杂矩阵求逆运算,加快了计算速度.将该方法应用于船舶操纵性模型非线性系统辨识,仿真结果表明,由该方法得到的LSSVR能够有效地对系统进行建模,仿真精度高,结构简单,具有一定的理论推广意义.

Modeling a complex nonlinear system with particle swarm optimization and parallel-optimized least squares support vector regression

Complex nonlinear systems usually suffer from high-order nonlinearity and uncertainty of parameters.This makes it difficult to establish an accurate mathematical model using conventional identification methods.To solve this problem,a new least squares support vector regression based on particle swarm optimization (PSO-LSSVR) was proposed.This identification model used two PSOs in parallel.One automatically sets the parameters of the LSSVR,while the other iterates the matrix.Thus the precision of identificat...