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1.
运用混合回归模型预报赤潮   总被引:5,自引:2,他引:3  
通过先行建立每个影响因子的非线性回归模型,然后作总体线性回归,首次给出了用混合回归模型预报赤潮的方法。这样就改进了现有的用多元线性回归模型进行赤潮预报的方法,提高了赤潮预报的准确性。  相似文献
2.
投影寻踪门限自回归模型在海洋冰情预测中的应用   总被引:5,自引:0,他引:5  
为预测海洋冰情时序这类非线性动力系统,提出了投影寻踪门限自回归(PPTAR)模型。用自相关分析技术确定预测因子,构造了新的投影指标函数,用门限回归(TR)模型描述投影值与预测对象间的非线性关系,并用实码加速遣传算法优化投影指标函数和TR模型参数。实例的计算结果表明,用PPTAR模型预测海洋冰情时序是可行和有效的,PPTAR模型简便,适用性强,克服了目前投影寻踪方法计算量大,编程实现困难的缺点,有助于投影寻踪方法的推广应用。为解决非线性时序复杂预测问题提供了新的途径。  相似文献
3.
多波束相位差序列多项式回归模型阶次的确定   总被引:4,自引:0,他引:4  
介绍了用方差分析确定正交多项式的步骤,通过仿真数据证明平坦海底的多波束相位差序列多项式模型是二阶的,而实际海底的相位差序列多项式模型是一阶的或是二阶的。应用方差原则来选择相应的阶次。  相似文献
4.
防范海洋灾害风险提高渔民收入促进农民增收   总被引:1,自引:0,他引:1       下载免费PDF全文
青岛市渔民收入与农民收入间关系的回归模型显示,前者对后者有着很大的促进作用,渔民收入提高可以从总体上增加农民的收入水平.而采取有效措施切实规避制约养殖渔业发展的海洋灾害、养殖病害、市场风险和引苗育种瓶颈四大问题,是促进渔民收入提高的关键所在.在用数据统计分析方法探寻渔民收入与农民收入相关关系的基础上,提出了解决海洋灾害问题的政策措施,以求有效阻止青岛市渔民收入连年递减的趋势,进而切实保障青岛市农民收入的持续增加.  相似文献
5.
以珠江口东岸香港海域为研究对象,准同步获取实测悬浮物浓度和Radarsat-2影像数据.对影像进行滤波处理和掩模处理后,利用Radarsat-2四种极化下的后向散射系数建立悬浮物浓度单极化回归模型;通过多极化后向散射系数构造多个遥感参数,运用相关性分析得到4个敏感因子,建立悬浮物浓度多极化回归模型.最终得到研究区域悬浮物浓度的反演模型为:SSC=11.08+0.06(HH+VV)-0.002(HH+VV)2,R2=0.84,其中SSC为悬浮物浓度,HH和W为该极化下的后向散射系数,R2为决定系数.研究表明:HH和W极化的后向散射系数之和对研究区域悬浮物反演最为敏感,得到的反演模型能较好预测海洋悬浮物的分布情况.  相似文献
6.
以吴淞站1955-2001年月平均潮位序列为基础,采用奇异谱分析(SSA)与自回归模型(AR)相结合的方案(SSA AR),进行了月平均潮位预测试验。基本思路是对SSA分析的结果选择若干有意义的分量进行序列重建,借助于自回归模型进行分量预测,再对它们进行叠加,从而建立预测模型。本文以1955-1996年数据为基础建立模型,1997-2001年数据作为验证,检验结果表明,两种方法的结合使用显示了较好的效果。  相似文献
7.
Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1 ) and potential energy (X2 ) significantly impact (p 〈 0. 0001 ) the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height ( X1 ) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers ( R2, Max-rescaled R^2, Sorners' D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.  相似文献
8.
Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.  相似文献
9.
研究一般的回归模型中误差方差的二次型估计的容许性,研究方法是模型的整体转化和局部转化,结果有:(1)二次约束下的线性模型等价于相应的无约束的线性模型。(2)线性(齐次或非齐次)等式约束下的线性模型等价于某个无约束的线性模型。(3)单个非齐次不等式约束下的线性模型等价于某个无约束的线性模型。(4)通过例子证明了多个线性不等式约束的线性模型不能等价于某个无约束的线性模型。(5)某类非齐次二次型估计的容许性等价于相应的齐次二次型估计的容许性  相似文献
10.
Due to the geological complexities of ore body formation and limited borehole sampling, this paper proposes a robust weighted least square support vectormachine (LS-SVM) regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each in complete sample. Then, a weighted K-nearest neighbor (WKNN) algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimization (PSO) algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by randomdivision. Coupled simulated annealing (CSA) and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight (IDW), ordinary kriging (OK), and back propagation (BP) neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the othermethods, and has strong predictive and generalization ability.  相似文献
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