在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时...在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时将Q作为适应度函数;然后利用微分进化算法(Differential Evolution Algorithm,DE)对核参数进行寻优,以此选择确定核参数;最后利用该算法建立RVM回归模型对黄金价格进行短期预测。实验结果表明,该模型较传统方法建立的预测模型具有更高的拟合精度和更好的泛化能力,进一步证明基于AIC准则选择RVM的核参数的方法的可行性和有效性。展开更多
This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, ...This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.展开更多
文摘在采用高斯径向基函数的相关向量机(RVM)回归模型中,核参数与模型性能之间关系复杂,针对如何确定RVM核参数的问题,提出一种基于AIC准则选择RVM的核参数的方法。首先基于Akaike Information Criterion(AIC)思想,得出一种新的统计量Q,同时将Q作为适应度函数;然后利用微分进化算法(Differential Evolution Algorithm,DE)对核参数进行寻优,以此选择确定核参数;最后利用该算法建立RVM回归模型对黄金价格进行短期预测。实验结果表明,该模型较传统方法建立的预测模型具有更高的拟合精度和更好的泛化能力,进一步证明基于AIC准则选择RVM的核参数的方法的可行性和有效性。
基金supported by the National Natural Science Foundation of China(61172159)
文摘This paper extends the application of compressive sensing(CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search,and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal.The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter(AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.
