Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf colo...Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.展开更多
A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and le...A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.XDJK2019D041)the Research Innovation Programs for graduate student of Chongqing,China(Grant No.CYS19123)the National Undergraduate Innovation and Entrepreneurship Training Programs(Grant No.201810635015).
文摘Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of plants.In this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(SVM)algorithms.Results reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not obvious.The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of C.coggygria in a shrubbery with a complex geographical environment in the absence of human activity.
文摘A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.
