基于改进LS-SVM的随钻测量数据传输误码率预测

针对泥浆连续波随钻测量数据传输误码率预测精度低、数据传输过程中易受干扰信号影响等缺点,提出利用改进的最小二乘向量积(LS-SVM)对连续波数据传输误码率建立预测模型,并引用遗传算法对参数寻优,在建立模型过程中利用狄克逊准则对数据进行筛选,从而提高误码率预测的精度.在小样本数据的情况下,采用Matlab建立基于改进的最小二乘支持向量机泥浆连续波数据传输模型.仿真结果表明该模型能够有效地避免陷入局部最优问题,具有较强的泛化能力和预测能力.通过与误差反传前馈(Back propagation,BP)和Elman神经网络预测模型对比可知,该模型预测精度更高,预测值更接近于实际值,可以用于泥浆连续波数据传输误码率预测.

Prediction of Error Rate in Measurement While Drilling Data Transmission Based on Improved LS-SVM Method

In the continuous wave measurement while drilling (MWD) system, the accuracy of error rate prediction is low and the data transfer process is affected by interference signals.A model for error rate prediction of continuous-wave data transmission is proposed by using the improved least squares support vector machines (LS-SVM), and the genetic algorithm is used to search the optimized parameter to improve the prediction accuracy of the model. During establishing the model, Dixon criteria is used to screen the data and improve the error rate prediction accuracy. With small samples, mud continuous-wave data transmission model is established by using Matlabbased on the improved LS-SVM. The simulation results show that the model can avoid falling into local optimization problem effectively, and has strong generalization and prediction ability. Compared with back propagation(BP) and Elman neural network, the model has higher prediction accuracy, so it can be used to predict the error rate of mud continuous-wave data.