盐湖水化学类型的人工神经网络判别方法

研究了作为典型径向基函数网络之一的概率神经网络在盐湖水化学类型分类预测中的应用，验证了该方法的可靠性，得到了满意的分类预测结果。实验结果和网络结构分析表明，概率神经网络方法比熟知的反向传播算法(BP)网络要好。概率神经网络的研究应用为化学模式识别提供了一个新工具。     

富勒烯碳-60,碳-70的电化学研究进展

短短的几年,随着各种物理与化学研究方法的相继介入,以C_(60)和C_(70)为代表的富勒烯研究取得了令人瞩目的成就。跟踪最新进展,引文41篇,本文就其电化学方面的研究工作做了系统的综述。     

基于冠心病心肌缺血血瘀证小型猪血清核磁共振代谢组学的研究

通过左冠状动脉前降支放置Ameriod环制备冠心病慢性心肌缺血动物模型,并结合冠脉造影等方法确立中医血瘀证证候,采用现代核磁共振(NMR)和模式识别技术,分析冠心病心肌缺血血瘀证模型与假手术组小型猪4星期血清的共振氢谱,及相应代谢物谱群的变化,开展中医血瘀证候学的研究.结果显示,该方法制备的模型4星期后确立为稳定的冠心...     

七-(2,6-二-O-甲基)-β-环糊精对薄荷醇对映体手性识别的电喷雾质谱研究

利用电喷雾质谱研究了七-(2,6-二-O-甲基)-β-环糊精(DM-β-CD)作为手性识别试剂对薄荷醇对映体的手性识别效应。实验结果表明,在气相条件下,DM-β-CD可以与薄荷醇形成特异性结合复合物,化学计量比为1∶1。对复合物的串联质谱研究表明,DM-β-CD对薄荷醇对映体有较强的手性识别能力,手性识别率为Rchiral=1.81。DM-β-CD与(-)-薄荷醇形成的复合物比与( )-薄荷醇形成的复合物稳定。     

毛细管电泳-小波神经网络法辅佐诊断乳腺癌的研究

采用毛细管电泳法测定了46个健康人和26个乳腺癌病人尿样中的13种正常核苷和修饰核苷,以小波神经网络作为模式识别工具对健康人和乳腺癌病人的分类作了研究,随机选取的训练集的识别率达到100％,相应的预测集判别率正确性在96％以上,与经典的前向多层神经网络相比,小波神经网络具有更强的信息提取和逼近能力.研究结果还表明,小波神经网络的预测能力强于主成分分析和线性判别分析,毛细管电泳法与小波神经网络的结合有望成为乳腺癌的辅助诊断手段.     

(S)-1,1-二苯基-2-吡咯啶甲醇催化蒽酮与马来酰亚胺的不对称Diels-Alder反应

研究了(S)-1,1-二苯基-2-吡咯啶甲醇催化蒽酮与马来酰亚胺的不对称D iels-A lder反应,考察了蒽酮与不同N-取代马来酰亚胺的反应。结果表明,在最佳反应条件下化学产率97%,对映选择性44%。     

(4R-cis)-6-[2-[2-(4-氟苯基)-5-(1-异丙基)-3-苯基-4-[苯胺(羰基)]-1 H-吡咯-1-基]乙基]-2,2-二甲基-1,3-二氧己环-4-乙酸叔丁酯的波谱学研究

对(4R-cis)-6-[2-[2-(4-氟苯基)-5-(1-异丙基)-3-苯基-4-[苯胺(羰基)]-^1H-吡咯-1-基]乙基]-2,2-二甲基-1,3-二氧己环-4-乙酸叔丁酯的傅里叶变换离子回旋共振质谱(FT-ICR-MS)、核磁共振氢谱(^1H-NMR)、碳谱(^13C-NMR)以及^1H同核位移相关谱(^1H-^1HCOSY)、检出^1H的异核多量子相干谱(HMQC)和^1H检测的异核多键相关谱(HMBC)报道并进行解析。确定了^1H谱、^13C谱中各谱峰的归属,研究了其六元环部分的立体构象,并就空间效应对其化学位移的影响做了初步的探讨。     

气相色谱-质谱法对当归及其炮制品挥发油的指纹图谱和潜在标志物研究

建立了一种基于气相色谱-质谱技术(GC-MS)的化学指纹图谱,以发现当归及其不同炮制品潜在标志物的方法.利用GC-MS获得当归及其不同炮制品挥发油化学指纹图谱,对产生的75样本×259变量数据进行归一化、修正80％规则和数据缩放等方法预处理,通过正交校正偏最小二乘法(OPLS)模式识别方法对样品进行模式识别,根据模型的变量重要性因子(VIP)和非参数检验结果筛选出12个潜在标志物.经相关分析和结构鉴定,其中11个化合物分别被鉴定为丁内酯、萜烯醇、6-丁基-1,4环庚二烯、2-壬酮、6-十一烷酮、2-甲氧基苯酚、δ-榄香烯、4,5,6,7-四甲基苯酞、Z-丁烯基酞内酯、亚油酸甲酯、1,7-异丙基-4-甲基-1,4,5,6,7,7a-六氢-2H-茚-2-酮.     

2,3,7-三羟基-9-(4,5-二溴-邻硝基)苯基荧光酮与钨(Ⅵ)的显色反应研究

研究了在阳离子表面活性剂溴化十六烷基三甲铵(CTMAB)存在下,2,3,7 三羟基 9 (4,5 二溴 邻硝基)苯基荧光酮(DBONPF)与钨(Ⅵ)的显色反应及光度性能。在0.4mol LH2SO4介质中,试剂与钨(Ⅵ)形成化学计量比为2∶1的红色络合物,其最大吸收峰位于547nm波长处,表观摩尔吸光系数为5.64×105L·mol-1·cm-1,检出限为4.12μg L,钨质量浓度在0～500μg L范围内符合比尔定律,分析方法可用于钢样中微量钨的测定。     

4,5-二氧-2-硫-1,3,2,4-二氮二磷杂环戊烷的合成和结构

本文通过氨基甲酰基膦酰胺酯与4-(取代)苯基二氯化膦缩含,然后硫化的方法制备了一类新型的双磷杂环化合物——4,5-二氧-2-硫-1,3,2,4-二氮二磷杂环戊烷。利用HPLC分离出了顺式和反式异构体,并分别测定了其晶体结构。通过~1HNMR和~(31)P NMR谱研究了化合物的立体结构,顺式和反式异构体有不同的化学位移和偶合常数。生物测定初步结果表明,这些化合物都有一定的除草活性,其中个别化合物有较高的除草活性。     

用神经网络法鉴别十二碳共轭双烯-1-醇、醛和乙酸酯双键位置异构体

将神经网络运用到十二碳双烯-1-醇、醛、乙酸酯三类化合物及其双键位置异构体的识别预报上,本文报道这一工作的主要结果.     

Hybrid neural modeling of bioprocesses using functional link networks

The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed, combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model, two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed.     

Use of neural networks in on-line analysis

The evaluation principles of neural networks are presented and compared with common known techniques. The concepts in data processing, introduced by neural nets are explained and processing types implemented by neural networks are presented. The evaluation of gas sensitive sensors will be an example for the special features of neural nets, with a focus to the self-organizing map.@peanuts.informatik.uni-tuebingen.de     

人工神经网络在纸浆卡伯值光学定量分析中的应用

卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460～ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…     

Modelling and prediction of retention in high-performance liquid chromatography by using neural networks

Summary Multi-layer feed-forward neural networks trained with an error back-propagation algorithm have been used to model retention behaviour of liquid chromatography as a function of the composition of the mobile phases. Conventional hydro-organic and micellar mobile phases were considered. Accurate retention modelling and prediction have been achieved using mobile phases defined by two, three and four parameters. With micellar mobile phases, the parameters involved included the concentrations of surfactant and organic modifier, pH and temperature. It is shown that neural networks provide a competitive tool to model varied inherent nonlinear relationships of retention behaviour with respect to the mobile phase parameters. The soft models defined by the weights of the networks are capable of accommodating all types of linear and nonlinear relationships, neural networks being specially useful when the relationships between retention behaviour and the mobile phase parameters are unknown. However, to train neural networks more experimental points than with hard-modelling methods are required, hence the use of the networks is recommended only for those cases where adequate theoretical or empirical models do not exist.     

氢键碱度的神经网络法计算

氢键在生命科学和化学等领域均起着十分重要的作用.化合物可以通过提供质子和接受质子等两种方式与其它化合物形成分子间氢键,其形成氢键的能力分别称为氢键酸度（hydrogen-bondacidity）和氢键碱度(hydrogen-bondbasicity).可以用正辛醇／水分配系数和环己烷／水分配系数的对数差（ΔlogP）[1]、溶剂化显色参数[2－3]等表示化合物形成氢键的能力,其中应用较多的是Abraham等[4]提出的总氢键酸度（）和总氢键碱度（）.但由于和要通过实验得到,繁琐不便,限制了它们的广泛应用.本文用神经网络法研究了理论计算得到的量子化学参数与之间的相…     

Identification of water samples from different springs and rivers of Kharkiv: Comparison of methods for multivariate data analysis

The application of artificial neural networks for identifying water samples from different springs and rivers of Kharkiv based on the data about metal ions concentrations was studied. Using the river-water samples as an example, we demonstrated that the artificial neural networks enabled the correct identification of water samples, even if there were some gaps in the initial data. The procedure for determining the optimal number of neurons for synthesizing neural networks was proposed.     

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.     

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.