均匀设计和神经网络优化高固量聚丙烯酰胺的合成工艺

通过均匀设计的方法探索合成高固含量低相对分子质量的聚丙烯酰胺的合成工艺，并利用人工神经网络对实验结果进行非线性拟合，通过筛选优化得到高浓度低相对分子质量聚丙烯酰胺的合成工艺条件．结果表明：通过均匀设计与人工神经网络结合，是一种很好地实验设计和优化实验条件的方法．     

人工深水湖渗流场及防渗方案三维仿真模拟研究

郑州市郑东新区龙湖规划水面面积为6．08km^2，为我国最大的城市人工湖。因其成湖地层主要为细砂，其建设存在开挖施工防渗、成湖后的有限防渗、地下水交换，以及防止周边地区浸没、盐渍化等问题。应用三维渗流有限元法数值模型对龙湖工程施工和运行期防渗方案进行了模拟仿真计算，给出了工程区地下水位、渗漏量、渗透坡降、渗流场的变化以及优化防渗方案。     

文化算法研究进展

文化算法是一种源于文化进化过程的进化模型,具有许多优良特性,成功地解决了一些复杂的问题,但该算法并不完善。本文介绍文化算法的模型及其研究进展,指出了该算法未来可能的研究方向。     

应用人工神经网络优化五点法水平井井网

水平井直井联合布井方式越来越多地应用到油田的实际开发中，但是井网的选择与优化方面的研究比较少，特别是没有根据不同开发阶段变化的生产动态对水平井井网进行优化。应用人工神经网络技术，在进行数值模拟计算的基础上，制作样本并训练网络，可预测任意面积、任意水平井长度下的五点法水平井井网在不同开发阶段的生产动态，优化出不同生产时间和不同井网面积条件下最优井网穿透比，即生产10a时最优井网穿透比为0．55～0．8。与数值模拟结果进行对比，最大误差在1％以内，表明这种方法既快速简便又具有较高的精确性，可以有效地指导五点法水平井井网优化设计。     

具有自发可逆形变特性的液晶弹性体

单畴结构的液晶弹性体具有自发可逆形变特性，是一种新型的智能材料，在传感器、微执行器等领域具有重要的应用价值．分别综述了热致型、光致型自发可逆形变液晶弹性体的研究发展现状，分析和阐述了单畴液晶弹性体自发可逆形变特性的原理，并展望了单畴液晶弹性体在人工肌肉和形状记忆材料方面的应用前景．     

基于BP神经网络进行裂缝识别研究

裂缝系统是个复杂的地质体，其储层物性（主要是渗透性）的改善作用是非线形的，各种评价参数与裂缝发育程度之间的关系也是非线形的，导致对裂缝进行定量评价十分困难，单纯依靠常规测井资料进行裂缝识别，存在主观不确定性及多解性；成像测井虽然直观准确，但成本较高。本文基于人工神经网络理论，开展了常规测井资料识别评价裂缝的研究。结果表明，基于BP神经网络的裂缝性储集层常规测井识别，与成像测井对比具有较好的应用效果。     

Artificial neural network modeling for predicting melt‐blowing processing

An artificial neural network (ANN) model is established for predicting the fiber diameter of melt‐blown nonwoven fabrics from the processing parameters. An attempt is made to study the effect of the number of the hidden layers and the hidden layer neurons to minimize the prediction error. The artificial neural network with three hidden layers (5, 2, and 3 neurons in the first, second, and third hidden layer, respectively) yields the minimum prediction error, and thus, is determined as the preferred network. The square of correlation coefficient of measured and predicted fiber diameters shows the good performance of the model. Using the established ANN model, computer simulations of the effects of the processing parameter on the fiber diameter are carried out. The results show great prospects for this research in the field of computer‐assisted design of melt‐blowing technology. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4275–4280, 2006     

Classifying Construction Contractors Using Unsupervised-Learning Neural Networks

Contractor prequalification involves the screening of contractors by a project owner to determine their competence to complete the project on time, within budget, and to expected quality standards. The process of prequalification involves a large number of contractors, each being represented by many attributes. A neural network model was applied to aid in the prequalification process by classifying contractors into groups based on similarity in performance using the financial ratios of liquidity, activity, profitability, and leverage. Contractors are represented in this model by patterns in four-dimensional space. Patterns of similar performance tend to form clusters intercepting regions of low pattern density in between. A neuron with weights is used as a classifier to set a decision boundary between clusters. The method basically iterates the neuron weights to move the decision boundary to a place of low pattern density. Then, the statistical hypothesis testing of the mean difference of two independent samples was used to validate the classification of the parent class to the two child classes considering the four ratios separately. The method was used hierarchically to classify a group of 245 contractors into classes of small numbers. Finally, the inferred procedure of classification proves that the neural network model classified the four-dimension pattern representing contractors efficiently.     

Artificial neural network modeling for predicting melt blowing processing

An artificial neural network model is established for predicting the fiber diameter of melt blown nonwoven fabrics from the processing parameters. An attempt is made to study the effect of the number of hidden layers and hidden layer neurons to minimize the prediction error. The artificial neural network with three hidden layers (5, 2, and 3 neurons in the first, second, and third hidden layer, respectively) yields the minimum prediction error and thus is determined as the preferred network. The square of the correlation coefficient of measured and predicted fiber diameters shows the good performance of the model. Using the established ANN model, computer simulations of the effects of the processing parameters on the fiber diameter are carried out. The results show great promise for this research in the field of computer assisted design of melt blowing technology. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 424–429, 2006     

Emotional intelligence in young and middle adulthood: Cross-sectional analysis of latent structure and means.

Differentiation of the construct of emotional intelligence was investigated in young and middle-aged adults, on the basis of hypotheses generated from differential emotions theory, discrete emotions functionalist theory, and empirical literature on age-related changes in affective complexity and differentiation of abilities. Both age groups were characterized by the same set of comparably related dimensions. However, midlife adults reported significantly greater use of optimism as a mood-regulation strategy than was reported by young adults. This study considers implications of possible structural continuity in emotional intelligence in conjunction with mean increases in the use of optimism as a strategy for managing affect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)