神经网络优化组合预测模型在油气产量预测中的应用

采用组合预测方法对油气产量预测进行研究,首先选取油藏工程领域多种油气产量预测模型建立组合预测模型库,基于权系数的时效性,利用三层前馈BP神经网络建立油气产量变权组合预测模型,并进行实例分析,结果表明该方法能提高预测精度,增强预测模型的实用性.     

Prediction of index futures returns and the analysis of financial spillovers—A comparison between GARCH and the grey theorem

This paper adopts the GM(1, 1) model to predict the rates of return of nine major index futures in the American and Eurasian markets. In a further step, by means of local grey relational analysis and by employing the GM(1, N) model for the first time, the variation relatedness and the main influencing factor among the above mentioned targeted markets is determined. Then, a comparison between GARCH/TGARCH and the grey theory with regard to predictive power is conducted. The findings reveal that the GARCH/TGARCH model performs better than the GM(1, 1), including the optimal α method, in terms of forecasting capabilities. Meanwhile, it is also found that GARCH and spillover effects indeed exist. Moreover, GM(1, N) also reveals that the daily rate of return of the Dow Jones index futures has the most influence on the rates of return of the other index futures.     

Recent advances in the structure elucidation of small organic molecules by the LSD software

The LSD software proposes the structures of small organic molecules that fit with structural constraints from 1D and 2D NMR spectroscopy. Its initial design introduced limits that needed to be eliminated to extend its scope and help its users choose the most likely structure among those proposed. The LSD software code has been improved, so that it recognizes a wider set of atom types to build molecules. More flexibility has been given in the interpretation of 2D NMR data, including the automatic detection of very long‐range correlations. A program named pyLSD was written to deal with problems in which atom types are ambiguously defined. It also provides a ¹³C NMR chemical shift‐based solution ranking algorithm. PyLSD was able to propose the correct structure of hexacyclinol, a natural product whose structure determination has been highly controversal. The solution was ranked first within a list of ten structures that were produced by pyLSD from the literature NMR data. The structure of an aporphin natural product was determined by pyLSD, taking advantage of the possibility of handling electrically charged atoms. The structure generation of the insect antifeedant azadirachtin by LSD was reinvestigated by pyLSD, considering that three ¹³C resonances did not lead to univocal hybridization states. Copyright © 2013 John Wiley & Sons, Ltd.     

基于支持向量机模型的建筑物沉降预测

针对建筑沉降发生的过程,采用支持向量机(SVM)模型对建筑物沉降进行预测.使用前期施工过程中的沉降观测数据作为训练样本集,建立现场动态沉降量预报模型.仿真试验和实践结果表明,模型与BP神经网络预测模型相比能够更准确地反映实际沉降过程,且满足精确性和适用性的要求.     

Model choice and squared prediction errors in PLS regression

Squared prediction errors (SPE) in are discussed in relation to the conventional PLSR versus bidiagonalization model and algorithm issue concerning residual and prediction consistency, with focus on process monitoring and fault detection. Our analysis leads to the conclusion that conventional PLSR based on the NIPALS algorithm is ambiguous in SPE values caused by process faults. The basic reason for this is that the sample residuals are not found as projections onto the orthogonal complement of the space where the scores and regression solution are located, and where also the statistical limit is defined. The alternative non‐orthogonalized PLSR and bidiagonalization (Bidiag2) algorithms, as well as a simple re‐formulation of the NIPALS algorithm (RE‐PLSR), give unambiguous SPE values, and the last two of these also retain orthogonal score vectors. While prediction results from all of these methods in theory are identical, our conclusion is that methods where the and SPE values for process faults are uncorrelated should be preferred. Tests with added errors on real data do not indicate that this conclusion should be altered because of such errors. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimization Based on Retention Prediction and Information Theory for Liquid-Chromatographic Analysis of Alkylbenzenes

Abstract

The mobile phase composition and column length are optimized for analyses of six alkylbenzenes in reversed-phase liquid chromatography with the aid of retention prediction and information theory. Optimal conditions selected according to the resolution Rs and information theory are evaluated from the viewpoint of the precision and analytical efficiency (rapidity) of chromatography. The combination of the information-theoretical optimization with the retention prediction will accelerate the development in the automation of liquid-chromatographic analysis.     

A reliable and efficient first principles‐based method for predicting pKa values. 4. organic bases

The ionization (dissociation) constant (pK_a) is one of the most important properties of a drug molecule. It is reported that almost 68% of ionized drugs are weak bases. To be able to predict accurately the pK_a value(s) for a drug candidate is very important, especially in the early stages of drug discovery, as calculations are much cheaper than determining pK_a values experimentally. In this study, we derive two linear fitting equations (pK_a = a × ΔE + b; where a and b are constants and ΔE is the energy difference between the cationic and neutral forms, i.e., ΔE = E_neutral?E_cationic) for predicting pK_as for organic bases in aqueous solution based on a training/test set of almost 500 compounds using our previously developed protocol (OLYP/6‐311+G**//3‐21G(d) with the the conductor‐like screening model solvation model, water as solvent; see Zhang, Baker, Pulay, J. Phys. Chem. A 2010 , 114, 432). One equation is for saturated bases such as aliphatic and cyclic amines, anilines, guanidines, imines, and amidines; the other is for unsaturated bases such as heterocyclic aromatic bases and their derivatives. The mean absolute deviations for saturated and unsaturated bases were 0.45 and 0.52 pK_a units, respectively. Over 60% and 86% of the computed pK_a values lie within ±0.5 and ±1.0 pK_a units, respectively, of the corresponding experimental values. The results further demonstrate that our protocol is reliable and can accurately predict pK_a values for organic bases. © 2012 Wiley Periodicals, Inc.     

基于脑模型控制器的红外背景预测算法

在分析云背景红外图像空间分布上存在混沌现象的基础上,提出一种基于脑模型控制器的红外背景预测算法.该方法利用混沌具有短时可预测性的特点,对云背景图像进行预测,并根据云背景杂波和运动目标的混沌特性差异修正预测模型.仿真结果表明,该算法能有效地提高云背景的预测准确度,预测残差符合白噪音特性,对云背景杂波具有良好的抑制效果,能显著提高目标的信杂比,从而改善目标的检测性能.     

Predicting extreme rainfall over eastern Asia by using complex networks

A climate network of extreme rainfall over eastern Asia is constructed for the period of 1971-2000, employing the tools of complex networks and a measure of nonlinear correlation called event synchronization （ES）. Using this network, we predict the extreme rainfall for several cases without delay and with n-day delay （1 ≤ n ≤ 10）. The prediction accuracy can reach 58% without delay, 21% with 1-day delay, and 12% with n-day delay （2 ≤ n ≤ 10）. The results reveal that the prediction accuracy is low in years of a weak east Asia summer monsoon （EASM） or 1 year later and high in years of a strong EASM or 1 year later. Furthermore, the prediction accuracy is higher due to the many more links that represent correlations between different grid points and a higher extreme rainfall rate during strong EASM years.