相似-动力模式的季节预报试验

本文从动力学角度,把要预报的场视为叠加在历史相似上的一个小扰动,建立了一个以动力学为基础考虑了地气之间耦合相互作用的相似-动力季节长期数值预报模式.利用该模式进行了1981—1988年共8年的季节预报试验.预报均由冬季1月开始预报至夏季8月.预报结果的统计表明,模式具有一定的季节预报能力,其预报准确率高于统计相似预报.     

一个预报西北太平洋台风移动的Markov型相似模式

本文建立了一个Markov型相似模式,用于预报西北太平洋热带气旋的移动.在概率求解方法上部分吸取了美国大西洋飓风预报的HURRAN方法.本模式跟传统的相似求解方法与概念是有原则区别的,为了使相似的选择能不断逼近和适应新的资料,采用Markov过程来描写台风移动的概率预报问题.72小时预报分6步来作,每步预报起始资料和相似概率求解资料都在不断更新。 为试验模式性能,文中还移植了传统的相似法,建立了预报太平洋台风移动的相似模式(方案1),并将方案1与Markov型相似模式(方案2)做相同实例的平行对比预报.对1981和1982年共95次台风的独立资料,进行了72小时预报试验.结果表明,方案2预报误差明显减小。     

多元金属熔体黏度预报几何模型及分析比较

提出了三种基于热力学通用几何模型预报多元金属熔体黏度的数学模型. 推导了各预报模型的黏度计算公式. 用Ag-Au-Cu三元金属熔体黏度的实验数据对三种模型进行了验证和比较, 分析了各模型的优缺点、适用范围和基本要求, 讨论并推导了各模型在特定条件下的扩展应用. 在此基础上预报了Cu-Ag-Sn三元金属熔体的黏度, 各模型的预报结果基本一致, 实验测定了Cu-Ag-Sn的部分黏度值, 结果与预报值吻合较好.     

一种新的预报策略

本文提出一种新的预报策略，将校准和预报的测量数据结合在一起进行主成分分解，找到校准和预报的共同的正交投影空间。这种新策略用于确定主成分数的交叉验证时只需一次主成分分解，因此速度很快；用于定量预报时，模型稳定性好，预报准确度令人满意。     

选择岭回归参数k值的改进方法及其在农药定量构效关系中的应用

提出改进的预报相对误差法选择岭回归参数ｋ，以平均预报相对误差替代预报残差平方和，并抑制过拟合。该方法应用于苯乙酰胺类除草农药定量构效关系的二次建模，效果良好，预报精度优于残差平方和方法。     

短波近红外光谱技术对葡萄酒中总糖含量快速测定的研究

采用短波近红外光谱技术结合偏最小二乘法(PLS),建立了葡萄酒中总糖含量的定量分析数学模型,讨论了光谱预处理方法和主成分数对PLS模型预报精度的影响.应用所建模型对预测集样本中总糖含量进行预报,结果令人满意.该方法方便快捷,并且具有较高的预报精度,可以用于葡萄酒中总糖含量的快速测定.     

华北六省空气监测数据将联网

<正>2014年上半年,一项基于AQI(空气质量标准)的预警预报系统或将立项。这套预警预报系统主要的出发点是为了增强预报预警的能力。据市环保监测中心负责人介绍,预报未来一段时期之内的空气质量情况,很多时候需要数值模型来进行模拟,虽然现在有一定的基础,但是新的平台建设完成后,计算速度会更快,覆盖区域更大,划分的网格会更精细,预报的时段会更长。"现在虽然是相当长时间之后的空气质量都能预报,但时间越长越不准。"该负责人表示,一般情况下,1到3天内的预报是可用作参考的。该系统投入使用后,3到4天甚至     

预报模式的参数优化方法

本文给出了一个适用于复杂的业务数值天气预报模式的参数反演方法。由此建立起参数反演系统后,原模式的程序几乎可以不作修改即成为该系统的一个子程序。通过多次调用这个子程序可得到最优的参数估计值。在一些较简单的模式上进行的数值模拟试验证实了此方法的有效性。此法可用来确定数值预报模式物理过程参数化中引入的各种参数的相互协调的数值,使模式参数调试实现客观化、自动化。同时还可用来对业务预报作“适时校准”,即根据最近的观测资料提供的信息,一旦发现预报误差较大时,及时修正模式中的一些参数以改进预报。     

酪氨酸激酶抑制剂的比较分子场分析

采用比较分子场分析(CoMFA)方法研究了一组嘧啶类衍生物酪氨酸激酶抑制剂活性与结构的关系.所得模型不仅能够很好地预报训练集中的化合物的活性,而且还可以准确地预报预报集中的化合物活性.通过分析分子场等值面图在空间的分布,可以观察到叠加分子周围的立体和静电特征对化合物活性的影响.     

人工神经网络-紫外光谱定量多组分体系的研究

本文系统地考察了人工神经网络(ANN)-紫外光谱(UVS)同时定量多组分混合溶液时参数选择对网络训练和预报性能的影响.合理选取诸参数,可提高训练效率改善预报性能,而且所优化的参数集可移植到其它相似体系。     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.     

烃类混合气体的神经网络模型检测

八十年代末科学家模仿生物鼻研制一种传感器阵列与计算机模式识别的气体检测系统．传感器阵列相当于生物鼻的嗅觉细胞，计算机模式识别系统相当于嗅泡和大脑「‘］．传感器阵列对气体的响应是一个多维空间的响应模式，这种响应模式经过一定的数学处理后可以实现气体的种类识别或浓度检测［’－‘j．传感器的响应和混合气体浓度之间呈非线性关系，这一特性给定量检测多组分气体混合物造成很大的限制．应用人工神经元网络技术（ANN）可以克服这一缺陷，并使检测气体的选择性大大提高．本工作运用ANN中的反向传播（BP）算法识别由16个不同…     

Evaluation of chemical equilibria with the use of artificial neural networks

Multivariate calibration with experimental design (ED) and artificial neural networks (ANN) modeling can be used to estimate equilibria constants from any kind of protonation or metal–ligand equilibrium data like potentiometry, polarography, spectrophotometry, extraction, etc. The method was tested on evenly or randomly distributed experimental error-free data and data with random noise and the results show that even rather higher experimental errors do not influence significantly the prediction power and correctness of ANN prediction. ANN with appropriate ED can provide accurate prediction of stability constants with the relative errors in the range of ±4% or smaller while the approach is very robust. Comparison with a hard model evaluation based on non-linear regression techniques shows excellent agreement. Proposed ANN method is of a general nature and, in principal, can be adopted to any analytical technique used in equilibria studies.     

人工神经网络方法用于多组分分光光度分析

本文介绍了人工神经网络（ANN）原理,详细讨论了网络参数的选择及其对网络预报结果的影响。并将人工神经网络应用于干扰严重的五组分体系的分光光度同时分析,其预测结果优于正交分解计算方法。     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors

The objective of this study was to investigate the predictability of an electronic nose for fruit quality indices. Responses signal of sensor array in electronic nose were employed to establish quality indices model for “xueqing” pear. The relationships were established between signal of electronic nose and the quality indices of fruit (firmness, soluble solids content (SSC) and pH) by multiple linear regressions (MLR) and artificial neural network (ANN). The prediction models for firmness and soluble solids content indicated a good prediction performance. The SSC model by ANN had a standard error of prediction (SEP) of 0.41 and correlation coefficient 0.93 between predicted and measured values, the model by ANN for the penetrating force (CF) had a 3.12 SEP and 0.94 coefficient, respectively. The results imply that it is possible to predict “xueqing” pear quality characteristics from signal of E-nose.     

傅里叶变换红外光声光谱法测定土壤中有效磷

以中国科学院封丘生态实验站长期定位实验区的土样为材料(68样),利用傅里叶转换红外光声光谱测定土壤有效磷:以Olsen-P为因变量,通过傅里转换红外光声光谱构建偏最小二乘法和人工神经网络模型,利用模型进行预测。结果表明,偏最小二乘法模型的相关系数(R2)为0.96,校正标准偏差为1.79mg/kg,验证标准偏差为5.25mg/kg;人工神经网络模型的校正系数为0.84,校正标准偏差为2.40mg/kg,验证标准偏差为5.43mg/kg。两种模型均可以用于土壤有效磷的预测,且偏最小二乘模型优于人工神经网络模型。该方法的特点是无需样品前处理,且测定对样品无破坏,为土壤有效磷的快速测定提供新的手段。     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.