CAJ | 학술논문

高斯过程（GP）的非线性特征导致其对大样本的训练时间复杂度过高，而且其超参数的选取是否适当直接影响高斯过程回归模型的预测精度。提出采用人工蜂群（ABC）算法优化改进GP以减小时间复杂度和提高预测精度。改进GP通过选取训练样本的子样本进行模型学习，以降低训练过程的时间复杂度。ABC通过优化改进GP的超参数，提升预测精度。选取训练样本的子样本构建改进GP回归（GPR）模型，采用ABC算法搜寻改进GPR的最优超参数，并用得到的超参数构建最优的改进GPR模型，输入测试样本进行预测并输出预测精度。将该模型应用于解决海上远程精确打击（LPSS ）体系作战效能评估问题中，通过MATLAB仿真实验，与常见的多种优化方法相比较，验证了该模型的有效性。
고사과정（GP）적비선성특정도치기대대양본적훈련시간복잡도과고，이차기초삼수적선취시부괄당직접영향고사과정회귀모형적예측정도。제출채용인공봉군（ABC）산법우화개진GP이감소시간복잡도화제고예측정도。개진GP통과선취훈련양본적자양본진행모형학습，이강저훈련과정적시간복잡도。ABC통과우화개진GP적초삼수，제승예측정도。선취훈련양본적자양본구건개진GP회귀（GPR）모형，채용ABC산법수심개진GPR적최우초삼수，병용득도적초삼수구건최우적개진GPR모형，수입측시양본진행예측병수출예측정도。장해모형응용우해결해상원정정학타격（LPSS ）체계작전효능평고문제중，통과MATLAB방진실험，여상견적다충우화방법상비교，험증료해모형적유효성。
Gaussian Process (GP)is characterized by the non-linear property,which leads to too high training time complexity for a large sample,And the hyper-parameters directly affect the prediction accuracy of Gaussian Process.The method of improved GP optimized by the artificial bee colony (ABC)algorithm is proposed to reduce the time complexity and to improve the prediction accuracy.Improved GP constructs the model by selecting a sub-sample of training samples to reduce training time.ABC optimizes the hyper-parameters of improved GP to improve prediction accuracy.Firstly,the improved GPR model is constructed by selecting a sub-sample of training samples;then it is followed by ABC algorithm searching the optimal hyper-parameters of improved GPR;finally the test sample is used to predict and output the prediction accuracy. The model is applied to solve maritime long-range precision sea strike (LPSS)system-of-systems operational effectiveness evaluation issues,and the MATLAB simulation experiments verify the validity of the model compared with other evolutional algorithms.