Multi-objective meta level soft computing-based evolutionary structural design

Evolutionary structural design has been the topic of much recent research; however, such designs are usually hampered by the time-consuming stage of prototype evaluations using standard finite element analysis (FEA). Replacing the time-consuming FEA by neural network approximations may be a computationally efficient alternative, but the error in such approximation may misguide the optimization procedure. In this paper, a multi-objective meta-level (MOML) soft computing-based evolutionary scheme is proposed that aims to strike a balance between accuracy vs. computational efficiency and exploration vs. exploitation. The neural network (NN) is used here as a pre-filter when fitness is estimated to be of lesser significance while the standard FEA is used for solutions that may be optimal in their current population. Furthermore, a fuzzy controller updates parameters of the genetic algorithm (GA) in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of the membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane wing. General applicability of the resulting optimization algorithm is then tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fuzzy-GA-NN, a GA-NN, as well as a simple GA that only uses only FEA, in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms. Specifically, the proposed approach finds better structural designs more consistently while being computationally more efficient.     

一种基于径向基网络的读者流失分析模型

大量而频繁的读者流失导致了图书馆知识服务范围的减少,降低了图书馆信息服务的质量。本文根据读者资料和借阅行为及其相关数据等历史信息,将径向基神经网络应用于读者流失分析中,针对目前图书借阅中存在的问题,给出了读者流失预测模型,利用训练后的模型对读者的流失率进行预测并生成读者列表。该模型通过试验运行,取得了良好的效果,对大学图书馆的服务策略实施具有现实的指导意义。     

试论"NN"类名词与量词的双向选择

"NN"类名词由多个相关的名词性语素组成,名词性语素之间的关系具有复杂性,即"NN"之间有修饰关系、类属关系、同义关系等.这类名词与量词的搭配是否合理,与各自的语义特点和名词内部的结构有关,有很强的双向选择性.随着社会发展,这类名词越来越多,应重视这类词的语法特点.     

Social Media and Communications: Developing a Policy to Guide the Flow of Information

The use of social media is ubiquitous in the daily lives of nearly three quarters of the population in the United States. This article addresses the process and results of a policy development project for a National Network of Libraries of Medicine Regional Medical Library. Content, scheduling, editing, and author responsibilities are addressed for each of the chosen media outlets, as well as the preparation of prefatory material using an online social media policy development tool.     

一种基于自组织神经网络的中文文本聚类新方法

针对传统K—均值等算法在文本聚类中的缺陷 ,本文提出了一种树形动态自组织映射 (TGSOM)神经网络来实现中文文本聚类 ,克服了传统的K—均值等算法中文本种类需要预先给定的缺点。本文详尽描述了该网络模型的生成算法和算法中扩展因子的作用 ,并阐述了中文文本的数字化方法———TF .IDF .IG方法     

对NN/LM 2001-2006年合同期项目的定量分析及启示

为深入了解NN/LM的项目资助情况,文章对NN/LM网站上获得的上一五年合同期详细的项目信息进行了深入的分析,得到项目数、各区域分布特点、基金投入的分布特点等,并对基金支持的多少以及一些特殊项目进行了详细的分析.从中得到值得我国建立医学图书馆资源共享网络借鉴的经验和启示.     

自组织特征映射网在人力资源开发中的应用

随着知识经济时代的到来,人才战略越来越成为企业核心竞争力的源泉,发现和选聘高素质创造性人才成为人力资源开发与管理的关键环节。本文采用具有自组织映射能力的SOFM网络,选取某重点大学2003届毕业生进行分类研究,以揭示不同学科科目成绩与学生能力和知识水平之间的内在规律,从而实现对学生的全方位综合评价,该研究结果为企业选聘人才和用好人才提供了有益的参考。     

基于Gabor小波的维吾尔文笔迹的特征提取方法研究

本文提出使用Gabor小滤提取维吾尔文笔迹的纹理特征方法。首先,通过预处理形成维吾尔文纹理图像。按照维吾尔文笔迹的特点,对Gabor滤波器进行改进。然后用一组Gabor滤波,过滤后获得图像的高维特征向量。最后,最邻近分类器（NN）分类器对不同17名人的维吾尔文笔迹样本进行分类实验,并获得79.8%的鉴别率。     

基于SOFM网络的快速信息检索

本文根据SOFM网络的聚类功能,提出了一个基于SOFM网络的快速信息检索模型。与向量空间模型相比,该模型的检索速度明显提高。最后通过示例验证了该模型的可行性。