已有的轨迹预测方法难以对移动对象运动轨迹进行准确地描述,尤其在复杂且不确定的车载自组织网络(vehicular ad hoc network)(也称车联网)环境中.为了解决这一问题,提出基于变分高斯混合模型(variational Gaussian mixture model,VGMM)...已有的轨迹预测方法难以对移动对象运动轨迹进行准确地描述,尤其在复杂且不确定的车载自组织网络(vehicular ad hoc network)(也称车联网)环境中.为了解决这一问题,提出基于变分高斯混合模型(variational Gaussian mixture model,VGMM)的环境自适应轨迹预测方法 ESATP(environment self-adaptive prediction method based on VGMM).首先,在传统高斯混合模型的基础上使用变分贝叶斯推理近似方法处理混合高斯分布;其次设计变分贝叶斯期望最大化算法学习计算高斯混合模型参数,有效运用参数先验信息得到更高精度预测模型;最后,针对输入轨迹数据特征,使用参数自适应选择算法自动调节参数组合,灵活调整混合高斯分量的个数和轨迹段大小.实验结果表明:所提方法在实验中表现出较高的预测准确性,可应用于车辆移动定位产品中.展开更多
A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.展开更多
文摘已有的轨迹预测方法难以对移动对象运动轨迹进行准确地描述,尤其在复杂且不确定的车载自组织网络(vehicular ad hoc network)(也称车联网)环境中.为了解决这一问题,提出基于变分高斯混合模型(variational Gaussian mixture model,VGMM)的环境自适应轨迹预测方法 ESATP(environment self-adaptive prediction method based on VGMM).首先,在传统高斯混合模型的基础上使用变分贝叶斯推理近似方法处理混合高斯分布;其次设计变分贝叶斯期望最大化算法学习计算高斯混合模型参数,有效运用参数先验信息得到更高精度预测模型;最后,针对输入轨迹数据特征,使用参数自适应选择算法自动调节参数组合,灵活调整混合高斯分量的个数和轨迹段大小.实验结果表明:所提方法在实验中表现出较高的预测准确性,可应用于车辆移动定位产品中.
基金Project(2010ZC13012) supported by the Aviation Science Funds of China
文摘A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
