CAJ | 학술논문

考虑蜡沉积影响因素的复杂性和最小二乘支持向量机在小样本预测方面的优势，基于最小二乘支持向量机预测的原理，通过优化最小二乘支持向量机的参数，建立了蜡沉积速率的预测模型，并对蜡沉积速率进行了预测。结果表明：该方法在样本数量较小时仍具有较高的精度，蜡沉积速率的预测值和实验值的吻合程度较好；最小二乘支持向量机建模时可以得到直观的函数表达式，而神经网络方法却不能得到模型的显式表达式，因此该方法具有明显的优势；应用径向基核（RBF）作为核函数时，不同初值的正则化参数γ和核函数宽度σ对预测结果具有较大影响，使用时应合理选择。
고필사침적영향인소적복잡성화최소이승지지향량궤재소양본예측방면적우세，기우최소이승지지향량궤예측적원리，통과우화최소이승지지향량궤적삼수，건립료사침적속솔적예측모형，병대사침적속솔진행료예측。결과표명：해방법재양본수량교소시잉구유교고적정도，사침적속솔적예측치화실험치적문합정도교호；최소이승지지향량궤건모시가이득도직관적함수표체식，이신경망락방법각불능득도모형적현식표체식，인차해방법구유명현적우세；응용경향기핵（RBF）작위핵함수시，불동초치적정칙화삼수γ화핵함수관도σ대예측결과구유교대영향，사용시응합리선택。
Considering the complexity of the influence factors of wax deposition and the advantage of least squares support vector machine in small sample prediction,based on the prediction principles of least squares support vector machine,by optimizing the parameters of least squares support vector machine,the prediction model of wax deposition rate was established and wax deposition rate was predicted. The method had higher accuracy when the samples were fewer,and the prediction results of wax deposition rate was in good agreement with the experimental data. The least squares support vector machine could get the intuitive function expression when it was used to establish the model of wax deposition rate,while neural network method could not get explicit expression. so this method has sufficient preponderance. When the RBF kernel function was used , different initial values of regularization parametersγand kernel bandwidthσhad a greater impact on the predicted results,so it should be used with care.