神经元网络用于PCDD定量构效关系的研究

研究了不同PCDD（全名Polychlorinateddioxin）同系物分子结构的表达及特征参数的选择,应用神经元网络方法对其分子结构与色谱保留值进行了关联。对49种PCDD同系物在DWS往上不同温度下保留时间进行了预测,结果95％以上的数据点相对误差小于10％,而80％以上的数据点相对误差小于5％。     

泛权网场结合的逻辑守恒性及其在人工神经网络中的应用

给出了泛权网场复合的几个定义,证明了泛权网场复合的一组有关逻辑守恒性的定理。结合均场模型,探讨了泛权网场在人工神经网络中的应用前景。     

The logic conservation of compositions between panweighted networks and panweighted fields and their application in ann

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城市交通管理中的出租车规划3

根据城市的发展情况以及类比其他城市的发展状况,对城市交通出行情况建立模型,对城市出租车的数量及乘坐人口做出预测;并针对目前的油价上涨给出租车行业带来的收入问题做出分析;建立最优化问题对驾驶员和顾客的满意进行描述,最终给出合理的价格调整方案.     

Genetic Algorithm Based Potential Selection in Simultaneous Voltammetric Determination of Isoniazid and Hydrazine by Using Partial Least Squares (PLS) and Artificial Neural Networks (ANNs)

The voltammetric behavior of isoniazid and hydrazine at an overoxidized polypyrrole modified glassy carbon electrode has been investigated. The obtained cyclic voltammograms showed that their oxidation peaks were overlapped and it is difficult to determine them individually from a mixture without separation. To overcome this limitation, a procedure was proposed for resolution of overlapped voltammetric signals from mixtures of isoniazid and hydrazine. In this procedure, genetic algorithm was used for the selection of potentials for partial least squares. A feed forward artificial neural network with back propagation error algorithm was used to process the nonlinear relationship between currents and concentrations of hydrazine and isoniazid. The proposed method was suitable for determination of isoniazid in pharmaceutical tablets and detection of hydrazine impurities in the same samples.     

模式识别用于压电晶体传感器阵列识别可燃物质

用７个压电晶体组成传感器阵列,每个晶体上分别涂有不同种类的气相色谱固定液,通过测定各种可燃物质燃烧时放出的混合气体来识别所燃物质,在识别中分别应用了人工神经网络法（ＡＮＮ）和逐步判别分析法（ＳＤＡ）。讨论了解决神经网络开始训练时不收敛或产生麻痹现象的方法,提出了训练数据选取的新方法－训练集逐步扩展法,实验证明：人工神经网络对被测物质的识别准确率达１００％,高于逐步判别分析法（８３％）。     

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.     

HIV-1逆转录酶抑制剂2-氨基6-苯磺酰基苄腈及其类似物活性OSAR研究

首先利用半经验AM I量子化学方法计算了54个2-氨基-6-苯磺酰基苄腈及其类似物的物理化学、电子结构、指示变量等共28个参数,然后使用偏最小二乘,穷举回归和混沌遗传乘法训练的人工神经网络方法建立了这些参数和其抑制H IV-1逆转录酶活性之间的定量构效关系模型,为设计、合成更高生物活性的该类化合物提供了理论参考。     

Predicting kinetic parameters for coal devolatilization by means of Artificial Neural Networks

The chemical percolation devolatilization (CPD) model has been shown to represent the devolatilization process of different coals and heating conditions with good accuracy. However, its use in computational fluid dynamics is limited because of its relatively high computational cost. Here, an Artificial Neural Network (ANN) based model for predicting coal devolatilization kinetics is developed based on a database constructed with the CPD model for a wide range of coals and heating rates. The heating rates and the information of ultimate and proximate analysis are chosen as inputs of the ANN model to consider the effects of coal types and heating conditions on coal devolatilization; the outputs are the kinetic parameters for the two-step kinetic model. The learning, validation, and application results show that the proposed ANN model has a competitive prediction capability on both the total volatile release and release rates when compared with the CPD model, but has obvious computational efficiency advantages. Furthermore, the relative impact of the coal type and heating rate on each kinetic parameter for coal devolatilization is quantitatively evaluated through the Garson equation. It is found that the heating rate has the strongest effect on the pre-exponential factor, while the coal types show significant influence on the activation energy and final yield of the two reactions in the two-step model.