一种改进的LS-SVM算法及其应用

 为了避免LS-SVM算法中存在的矩阵求逆问题，提出一种改进的LS-SVM算法，即利用改进PSO算法对LS-SVM算法中线性方程组进行迭代优化求解，这样既能加快算法训练速度和节省内存，又总能得到最小二乘解，提高计算精度。将此改进算法应用到长庆气田C井目的层井段进行气层识别，并与BP神经网络算法、经典的SVM算法和传统的LS-SVM算法比较，结果表明此算法识别精度高，收敛速度快，与试气结果吻合，效果显著。

An improved LS-SVM algorithm and application

In order to avoid the issue of inversing matrix existed in LS-SVM (Least Squares Support Vector Machines) algorithm,the paper presented an improved LS-SVM algorithm: using improved PSO (Particle Swarm Optimization) algorithm to iteratively and optimally solve the linear equation set in LS-SVM algorithm,which can not only speed the training of the algorithm and save the memory,but also get least square solution,raising the computational precision.Using the improved algorithm to recognize the gas-bearing bed in the target stratum of well-C in Changqing gasfield and for comparison with BP neural network algorithm,classic SVM algorithm and traditional LS-SVM algorithm,the results showed the algorithm is characteristics of higher recognition precision,fast convergence speed and coincident to the result of well test,having excellent effect.