Selecting Hyper-Parameters of Gaussian Process Regression Based on Non-Inertial Particle Swarm Optimization in Internet of Things
Selecting Hyper-Parameters of Gaussian Process Regression Based on Non-Inertial Particle Swarm Optimization in Internet of Things
复制标题
物联网中基于非惯性粒子群优化的高斯过程回归超参数选择
DOI：
10.1109/access.2019.2913757
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Chien-Ming
中科院分区：
文献类型：
--
作者：
Kang, Lanlan;Chen, Ruey-Shun;Chen, Chien-Ming
高斯过程回归（Gaussian process regression，GPR）是物联网数据中非平稳时间序列不确定性度量和预测的常用方法，超参数的选取直接影响GPR的泛化能力和回归效果。提出了一种精英变异-高斯过程回归的非惯性粒子群算法（NIPSO-GPR）来优化GRP的超参数。NIPSO-GPR通过统一的非惯性速度更新公式和自适应的精英变异策略，自适应地获取GPR超参数。在线性和非线性时间序列样本数据上与常用的几种超参数优化算法进行比较，实验结果表明，经NIPSO-GPR超参数优化后的GPR具有更好的拟合精度和泛化能力。
Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its selection of hyper-parameters. In the study, a non-inertial particle swarm optimization with elite mutation-Gaussian process regression (NIPSO-GPR) is proposed to optimize the hyper-parameters of GRP. NIPSO-GPR can adaptively obtain hyper-parameters of GPR via uniform non-inertial velocity update formula and adaptive elite mutation strategy. When compared with several frequently used algorithms of hyper-parameters optimization on linear and nonlinear time series sample data, experimental results indicate that GPR after hyper-parameters optimized by NIPSO-GPR has better fitting precision and generalization ability.