The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.
In the process of concept design of offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filter. The criterion set, used to evaluate and select the satisfying alternative, consists of many qualitative and quantitative factors. Therefore, the selection is a problem of multicriteria and semi-structural decision-making. Different from traditional methods in semi-structural decision-making, a new framework and methodology is presented in this paper for evaluation of offshore platform alternatives, First, the criterion set is established for the evaluation of alternatives. Next, the approach is studied to construct the relative membership degree matrix, in which both qualitative and quantitative factors are consistent with the uniform calculating standard. And then a new weight-assessing method is developed for calculation of the weights based on the relative membership degree matrix. Finally, a multi-hierarchy fuzzy optimum model is adopted to select the satisfying offshore platform alternative. A case study shows that the new framework and methodology are scientific, reasonable and easy to use in practice.
黄河内蒙段每年都有不同程度的凌汛灾害发生,准确及时的凌汛预报能够为防汛工作提供决策支持.但至今尚无一种令人满意的预测模型,为此提出一种基于支持向量机回归(SVR)的凌汛预报模型.SVR是基于统计学习理论的一种机器学习(m ach ine learn ing)方法,具有严格的理论基础,尤其是在小样本情况下,它能够利用有限的样本信息获得最好的学习效果和泛化能力.实例分析结果表明,基于SVR的凌汛预报方法具有训练速度快、泛化能力强的特点,对黄河内蒙段凌汛期封河历时预测比较准确,这对黄河凌汛防范和水资源的可持续发展具有重要意义.
