用电脑进行模糊聚类分析的研究

从ＦＵＺＺＹＳＥＴ出发，在电脑上用化合物中各元素的电负性、外层电子数、原子半径等参数进行模糊聚类分析，并对２０种化合物进行分类，得到了满意的结果。分析中提供了一种测定未知化合物类型的方法。该研究可视为计算化学的一种实例     

化学元素的模糊聚类分析

选用化学元素的密度、半径、燃烧焓、熔点、沸点、电负性及导热系数７个理化参数，运用系统模糊聚类分析方法，对化学元素周期表中的５０个元素进行了分类。分类结果大多与传统周期表匹配，但也表现出按外层电子结构排布规律所不能解释的现象，如锰为独立一组：Ｂｅ、Ｓｉ、ＡＩ为一组，Ｓi具有较明显的金属性等。     

基于电负性均衡方法准确计算杂环分子中的原子电荷

基于电负性均衡方法(EEM),依据原子类型和成键对原子进行细致划分,以准确计算分子中的原子电荷.为准确计算分子中原子的电荷,构建了包含214个分子、含各类不同基团的训练集,采用B3LYP/6-31g*基组对分子进行优化并得到各个分子中的原子Mulliken电荷.与其他校正方法将同一类原子视为拥有相同的有效电负性和有效硬度值不同,本文在EEM方程中,依据成键类型和化学环境变化,将C、N、O分别分为为4种、3种和2种不同类型,相应采用不同的电负性和硬度价态标度值,并采用全局优化算法对训练集进行优化以得到各类原子的EEM参数值.本文讨论了这些参数与其他方法所得到的参数的差异,并讨论了本方法与其他校正方法的区别.利用本文校正所得EEM方程参数,对不在训练集中的几个杂环分子的EEM电荷进行了计算,取得了较为满意的结果.     

第5,6周期元素原子轨道图及原子轨道半径

本文报导了第５、６周期元素原子轨道图及其原子轨道半径和原子界面半径，并将第１至第６周期元素的原子界面半径与最外层原子轨道电荷密度最大值的半径及有效原子半径进行了对比。     

考虑成本时拓扑可调无标度网络鲁棒性研究

为了研究无标度网络拓扑结构对网络鲁棒性的影响,结合对复杂网络鲁棒性有重要影响的节点介数和边权重两种指标,提出一种考虑成本的拓扑可调无标度网络攻击方法。该方法在攻击网络中节点（边）时引入了节点（边）的攻击成本因素,以节点介数（边权重）来近似衡量节点（边）的攻击成本,采用不同节点（边）攻击策略对网络进行攻击,并采用最大连通子图相对值作为网络鲁棒性测度指标,利用该方法对无标度网络的幂率指数、平均度与网络鲁棒性的关系分别进行了研究。结果表明,采用蓄意攻击策略时,对于同一节点（边）攻击成本,无标度网络的幂率指数越小或平均度越大,网络的鲁棒性越强。仿真实验验证了该方法的有效性与可行性。     

利用支持向量机研究钙钛矿型合金中间相的若干规律

以原子的金属半径、电负性和次内层d电子数为参数,用原子参数—模式识别方法,以及支持向量机等方法求得含碳、氮、硼的三元合金系形成钙钛矿型中间相的判据,并建立了计算钙钛矿型中间相的晶胞参数的半经验回归模型。该模型留一法的预报值正确率均在90%以上,与实验结果符合。     

氧分子在镍钛合金(100)表面吸附行为的研究

NiTi形状记忆合金作为一种广泛使用的生物医学材料，表面形成的氧化膜是其具有良好生物相容性的基础。氧分子在NiTi合金表面的吸附是其形成氧化膜的关键，应用离散变分Xα方法，首次对O2分子在B2结构NiTi(100)表面的吸附过程进行了理论研究，分别计算了在两种不同的O2分子吸附方式中Ti-O原子间的键级和电荷分布。结果表明：O2分子垂直接近NiTi(100)表面对其发生吸附更为有利。在吸附过程中，O2分子中只有一个氧原子被其最近邻的一个表面铁原子所吸附，而合金中其它表面原子及体相原子的电子结构没有变化。Mulliken集居数及局域态密度分析表明，吸附过程中铁原子与氧原子之间的相互作用主要是由2p(O)电子和4s，4p(Ti)电子贡献。     

AHP标度改进研究及其在IDS评估中的应用

传统的层次分析法的“1-9标度”方法不能精确的反映人的实际思维，尤其是在构权时可能会影响最终的判断结果，本文提出了一种基于三角模糊数表示的改进的AHP标度方法，使AHP构权更加符合人的比较思维。最后本文给出入侵检测系统评估指标体系构权实例，结果证明，与传统方法对比，应用三角模糊数方法构权能够更加精确的反应人的实际思维。     

有机化合物键极性诱导指数及其计算

一、前言有机化合物分子中,电子诱导效应与化合物的性质密切相关,深刻研究其间的关系有着重要的理论意义和实际意义。蒋明谦提出了基团诱导效应指数的概念及其计算方法。在此工作的基础上,我们增加了空间结构对诱导率的贡献,完善了带电原子共价半径与电负性的修正公式;对环状化合物提出等长最短诱导途径相容,非等长路取最短链传递的原则,给出一个寻找所有最短通路的简便方法;将[1]中基团诱导指数的概念扩充为化合物键极性诱导效应指数的概     

基于中介中心性提高复杂网络容量的方法

对于像互联网这样具有无标度特征的网络,节点的重要程度差别很大,少数中枢节点成为制约网络容量的瓶颈.引入中介中心性对网络拓扑进行优化和拥塞预测,通过理论分析和仿真实验,考察了网络中节点的介数的和、标准差,两点间最短路径长度,最短路径通过的中枢节点的个数等参数与网络容量的关系.最终提出在具有无标度特征的复杂网络中,依据网络中节点的介数以及介数的标准差增加一些捷径路径的方法.该方法简单易行,能有效平衡中枢节点的负载,缓解拥塞状况,提高网络容量.     

基于MATLAB软件的罗马数字模式识别

提出了利用神经网络的分类功能,并借助于MATLAB软件的神经网络工具箱,采用具有自适应学习速率和附加动量因子的BP型神经网络,实现罗马数字模式识别的新方法.该方法同样适用于其它符号的模式识别.     

磷系添加剂结构与润滑性能的量子化学研究

用HF／6-3lG方法全优化计算了次膦酸酯、膦酸酯、磷酸酯、亚磷酸酯、酸性磷酸酯、胺基磷酸酯、磷酸酰胺、磷酸酯胺盐等8类磷系润滑添加剂,用前线轨道能级,前线轨道电荷密度,原子净电荷,键级等参数,比较各类添加剂与金属间的相互作用,得到了添加剂的分子结构与其润滑抗磨性能的规律。计算表明,添加剂分子的前线轨道能级,Mulliken键级的数值决定了它们的润滑性能。根据磷系润滑添加剂的前线轨道能级,前线轨道电荷密度,原子净电荷以及Mulliken键级讨论了它们对钢一钢摩擦副,铝一铝摩擦副的润滑效果,计算结果与实验结论一致。     

Development of a hybrid PSO–ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R ²) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R ² of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model.

     

四氢咪唑苯二氮卓酮类抗艾滋病药物定量构效关系的研究

采用三维全息原子场作用矢量(3D-HoVAIF)研究89个四氢咪唑苯二氮卓酮(TIBO)类抗艾滋病药物的定量构效关系(QSAR).分别运用偏最小二乘回归、人工神经网络建模,同时采用内部及外部双重验证的办法深入分析和检验模型的稳定性.PLS与ANN建模的复相关系数(R2cum)、留一法(leave-one-out,LOO)交互校验(cross-validation,CV)复相关系数(Q2CV)和外部样本校验复相关系数(Q2ext),分别为0.802、0.710、0.552和0.871、0.864、0.760.表明用3D-HoVAIF表征TIBO类抗艾滋病药物分子结构信息较好,建立QSAR模型的稳定性和预测能力良好,运用ANN建模优于PLS及前人报道的多元线性回归(multiple linear regression,MLR).     

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.     

The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test

In this study, artificial neural networks (ANNs) were used to predict the settlement of one-way footings, without a need to perform any manual work such as using tables or charts. To achieve this, a computer programme was developed in the Matlab programming environment for calculating the settlement of one-way footings from five traditional settlement prediction methods. The footing geometry (length and width), the footing embedment depth, the bulk unit weight of the cohesionless soil, the footing applied pressure, and corrected standard penetration test varied during the settlement analyses, and the settlement value of each one-way footing was calculated for each traditional method by using the written programme. Then, an ANN model was developed for each method to predict the settlement by using the results of the analyses. The settlement values predicted from each ANN model developed were compared with the settlement values calculated from the traditional method. The predicted values were found to be quite close to the calculated values. Additionally, several performance indices such as determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to check the prediction capacity of the ANN models developed. The constructed ANN models have shown high prediction performance based on the performance indices calculated. The results demonstrated that the ANN models developed can be used at the preliminary stage of designing one-way footing on cohesionless soils without a need to perform any manual work such as using tables or charts.     

城市交通信号的ANN自校正预测控制

提出一种基于人工神经网络的城市交通信号的自校正预测控制方法.充分考虑相邻交叉路口之间交通流的强耦合性,在此基础上建立关于队长的交通模型;其中,受控路口下一周期到达的车辆数用人工神经网络(ANN)来预测;通过该ANN还可获得确定最佳周期长度所需要的交通参量,因此还可预测下一周期的长度;上述预测值均用实测信息进行反馈校正,在此基础上即可给出带约束的预测控制算法,从而确定下一周期的控制策略.仿真实例表明该方法具有较好的控制效果.     

Prediction of dissolved oxygen in River Calder by noise elimination time series using wavelet transform

Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash–Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.