岩石力学性态预测的PSO-SVM模型

 传统的固体力学方法在描述岩石的各种地质因素与其力学性态之间的复杂非线性关系时存在困难。引入粒子群算法(PSO)对支持向量机(SVM)进行优化，提出岩石力学性态预测的粒子群优化支持向量机模型(PSO-SVM)。该模型利用SVM来建立岩石地质因素与力学性态之间的非线性关系；同时利用PSO对SVM参数进行全局寻优，避免人为选择参数的盲目性，从而提高模型的预测精度。将PSO-SVM应用到岩石压缩系数的预测中，并与传统的BP神经网络(BP-NN)进行对比分析。结果显示，PSO-SVM的预测精度较BP-NN有较大的提高，从而表明PSO-SVM在岩石力学性态预测中的可行性和有效性。

FORECASTING OF ROCK MECHANICAL BEHAVIORS BASED ON PSO-SVM MODEL

It is difficult to describe the complex nonlinear relationship between all kinds of geological factors of rock and their mechanical behaviors. A new model for forecasting the mechanical behaviors of rock is proposed by combining the particle swarm optimization(PSO) and the support vector machines(SVM),which is support vector machine based on particle swarm optimization(PSO-SVM). The model,on one hand,uses the nonlinear characteristics of SVM to establish the nonlinear relationship between geological factors of rock and their mechanical behaviors. On the other hand,the penalty factor and kernel function parameter of SVM are optimized by PSO,by which the accuracy of the parameters used in the model is ensured as well as the precision of forecasting result. The model is applied to forecast the coefficient of compressibility of rock and the result is compared with that of back propagation neural network(BP-NN). It is shown that the forecasting precision of PSO-SVM is higher than that of BP-NN,which indicates that the model here is feasible and effective.