The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in...The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.展开更多
High-temperature rockbursts pose a critical challenge in deep underground engineering and resource exploitation.Consequently,predicting high-geothermal rockbursts has become a key scientific objective.In this paper,a ...High-temperature rockbursts pose a critical challenge in deep underground engineering and resource exploitation.Consequently,predicting high-geothermal rockbursts has become a key scientific objective.In this paper,a genetic projection pursuit algorithm(GPPA)is proposed for the prediction of high-geothermal rockbursts by introducing the coefficient K,and utilizing multiple empirical criteria(Wet index,σc/σt,σθ/σc,andσ1/σc).Four empirical criteria were statistically analyzed for 147 sets of rockburst cases,yielding accuracies of 40%,39%,46%and 29%,respectively.After the implantation of optimal segmentation,there was an enhancement in accuracy by 12%,9%,6%,and 19%,respectively.Theσθ/σc criterion exhibited superior performance,with a baseline accuracy of 46%.The GPPA model was tested and validated using four characteristic parameters(Wet index,σc/σt,σθ/σc,andσ1/σc)as inputs,revealing that the error ranged between 0.07 and 0.41.Successful validation was performed in the Sangzhuling Tunnel(four slight rockbursts)and Qirehataer Diversion Tunnel(one moderate rockburst),which matched field observations.Consequently,the proposed model offers guidance for predicting high-geothermal rockburst hazards.展开更多
基金supported by the Shan⁃dong Provincial Natural Science Foundation(No.ZR2022MF314).
文摘The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.
基金supported by the National Natural Science Foundation of China(Grant No.42130719)the Opening Foundation of Key Laboratory of Landslide Risk Early-warning and Control,Ministry of Emergency Management(Chengdu University of Technology)(Grant No.KLLREC2022K003)the Humanities and Social Sciences Youth Foundation,Ministry of Education(Grant No.23YJCZH051).
文摘High-temperature rockbursts pose a critical challenge in deep underground engineering and resource exploitation.Consequently,predicting high-geothermal rockbursts has become a key scientific objective.In this paper,a genetic projection pursuit algorithm(GPPA)is proposed for the prediction of high-geothermal rockbursts by introducing the coefficient K,and utilizing multiple empirical criteria(Wet index,σc/σt,σθ/σc,andσ1/σc).Four empirical criteria were statistically analyzed for 147 sets of rockburst cases,yielding accuracies of 40%,39%,46%and 29%,respectively.After the implantation of optimal segmentation,there was an enhancement in accuracy by 12%,9%,6%,and 19%,respectively.Theσθ/σc criterion exhibited superior performance,with a baseline accuracy of 46%.The GPPA model was tested and validated using four characteristic parameters(Wet index,σc/σt,σθ/σc,andσ1/σc)as inputs,revealing that the error ranged between 0.07 and 0.41.Successful validation was performed in the Sangzhuling Tunnel(four slight rockbursts)and Qirehataer Diversion Tunnel(one moderate rockburst),which matched field observations.Consequently,the proposed model offers guidance for predicting high-geothermal rockburst hazards.
文摘针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firefly algorithm,FA)收敛速度过慢以及易陷入局部最优等问题,引入教与学因子,修改萤火虫位置更新过程,以提高群体适应度。其次,引入IFA算法对BP神经网络模型进行超参数寻优,并构建IFA-BP神经网络预测模型。然后,基于CRITIC法筛选预测模型输入层的关键碳排放指标。最后,利用训练集数据训练预测模型,基于训练好的模型对变电站的碳排放量进行预测。仿真结果表明,相较于3种对比方案,本文IFA-BP神经网络预测模型分别在均方根误差(root mean square error,RMSE)上降低59.61%、15.77%和26.65%,在决定系数(coefficient of determination,R^(2))上提高5.66%、1.46%和1.15%,充分验证了本文所提变电站碳排放预测模型的可行性与优越性。
