To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target...To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.展开更多
Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in diffe...Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in different fields.In allusion to this,an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties.Firstly,knowledge base was established on triaxial compression testing data;then the model was trained,learned and emulated using knowledge base;finally,predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model.The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision,which provides possibility for engineering practice on demanding high precision.展开更多
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
随着城市化进程的加快,废土管理问题日益严峻。本文提出了一种基于固废胶凝材料的土壤固化方法,利用矿渣、钢渣和脱硫石膏作为固化剂,通过混料设计研究了不同配合比对砂质粉土无侧限抗压强度(UCS)的影响,并利用人工神经网络(ANN)建立了...随着城市化进程的加快,废土管理问题日益严峻。本文提出了一种基于固废胶凝材料的土壤固化方法,利用矿渣、钢渣和脱硫石膏作为固化剂,通过混料设计研究了不同配合比对砂质粉土无侧限抗压强度(UCS)的影响,并利用人工神经网络(ANN)建立了固化土UCS预测模型。结果表明,固废胶凝材料显著提升了固化土的早期和后期UCS,尤其是钢渣含量不高于30%(质量分数)时,固化土UCS优于水泥固化土。X射线衍射(XRD)和扫描电子显微镜(SEM)分析表明,水化产物钙矾石(AFt)和水化硅酸钙(C-S-H)凝胶填充了土壤孔隙,提升了固化土的密实度和力学性能。基于ANN构建的7、28 d UCS预测模型,决定系数R^(2)均在0.95以上,表现出较高的预测精度。通过特征重要性分析,胶凝材料掺量和土壤含水率对固化土UCS的影响较为显著。研究结果为固废胶凝材料的配合比优化和工程应用提供了理论依据。展开更多
基金Defense Industrial Technology Development Program (JCKY2020204B016)National Natural Science Foundation of China (92471206)。
文摘To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
基金Project(07031B) supported by the Scientific Research Fund of Central South University of Forestry and TechnologyProject(06C843) supported by the Scientific Research Fund of Hunan Provincial Education Department
文摘Application research of neural networks to geotechnical engineering has become a hotspot nowadays.General model may not reach the predicting precision in practical application due to different characteristics in different fields.In allusion to this,an elasto-plastic constitutive model based on clustering radial basis function neural network(BC-RBFNN) was proposed for moderate sandy clay according to its properties.Firstly,knowledge base was established on triaxial compression testing data;then the model was trained,learned and emulated using knowledge base;finally,predicting results of the BC-RBFNN model were compared and analyzed with those of other intelligent model.The results show that the BC-RBFNN model can alter the training and learning velocity and improve the predicting precision,which provides possibility for engineering practice on demanding high precision.
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
文摘随着城市化进程的加快,废土管理问题日益严峻。本文提出了一种基于固废胶凝材料的土壤固化方法,利用矿渣、钢渣和脱硫石膏作为固化剂,通过混料设计研究了不同配合比对砂质粉土无侧限抗压强度(UCS)的影响,并利用人工神经网络(ANN)建立了固化土UCS预测模型。结果表明,固废胶凝材料显著提升了固化土的早期和后期UCS,尤其是钢渣含量不高于30%(质量分数)时,固化土UCS优于水泥固化土。X射线衍射(XRD)和扫描电子显微镜(SEM)分析表明,水化产物钙矾石(AFt)和水化硅酸钙(C-S-H)凝胶填充了土壤孔隙,提升了固化土的密实度和力学性能。基于ANN构建的7、28 d UCS预测模型,决定系数R^(2)均在0.95以上,表现出较高的预测精度。通过特征重要性分析,胶凝材料掺量和土壤含水率对固化土UCS的影响较为显著。研究结果为固废胶凝材料的配合比优化和工程应用提供了理论依据。
