In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West ...In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West Azarbayjan, Ardebil and Zandjan were collected during 2003 and 2004. 575 and 564 images were prepared from fore and hind wings, respectively. Then 15 and 11 landmarks were determined from fore and hind wings, respectively. With transforming of landmark＇s two dimensional coordinate data into partial warp scores, 26 and 18 scores were generated for fore and hind wings, respectively. Cluster analysis based on wing shape variables using Ward＇s algorithm assigned nine geographical populations into two groups. The pattern of grouping based on fore and hind wings was different in both sexes. Principal component analysis revealed discrimination between geographic populations and confirmed the result of cluster analysis. Among environmental parameters, wind speed showed the highest correlation with wing shape variables. Non significant correlation was observed between geographic and morphological distance matrices as revealed by Mantel test.展开更多
Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performan...Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.展开更多
文摘In order to study morphological diversity of codling moth, Cydia pomonella （L.） using thin-plate spline analysis, nine geographical populations from four north western provinces of Iran namely East Azarbayjan, West Azarbayjan, Ardebil and Zandjan were collected during 2003 and 2004. 575 and 564 images were prepared from fore and hind wings, respectively. Then 15 and 11 landmarks were determined from fore and hind wings, respectively. With transforming of landmark＇s two dimensional coordinate data into partial warp scores, 26 and 18 scores were generated for fore and hind wings, respectively. Cluster analysis based on wing shape variables using Ward＇s algorithm assigned nine geographical populations into two groups. The pattern of grouping based on fore and hind wings was different in both sexes. Principal component analysis revealed discrimination between geographic populations and confirmed the result of cluster analysis. Among environmental parameters, wind speed showed the highest correlation with wing shape variables. Non significant correlation was observed between geographic and morphological distance matrices as revealed by Mantel test.
基金supported by the National Natural Science Foundation of China(61871420)the Natural Science Foundation of Sichuan Province,China(23NSFSC2916)the introduction of talent,Southwest MinZu University,China,funding research projects start(RQD2021064).
文摘Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.
