Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

基于PCA的SOM网络在基因数据聚类分析中的应用

把主分量分析（PCA）方法和自组织特征映射网络（SOM）相结合,应用到基因数据聚类分析中。首先对基因数据集进行PCA分析,提取出少量的特征主分量,再对数据集进行降维。这些主分量基本上可以反映原数据集的综合信息,然后应用SOM网络对得到的特征分量进行聚类分析,把相似的基因划分到一个区域。实验结果表明,与单一地选用SOM网络进行聚类分析相比,该方法有较高的分类正确率及较为清晰的分类边界,是一种非常有效的聚类分析方法。     

A novel self-organizing E-Learner community model with award and exchange mechanisms

INTRODUCTION Internet technology has enabled students tohave access to a web-based learning environment,which provides students and teachers with un-precedented flexibility and convenience. On theother hand, it also creates many lonely learners,which mean those who cannot share their class-mates’ learning experience and resources like intraditional classroom-based education. While a lotof research has been pursued to provide collabora-tive learning environments for geographically d…     

基于系统科学的温州企业成长系统发展研究

运用系统分析方法,结合实例对温州企业成长系统发展进行理论与实证分析,力求探索温州企业成长系统发展的一般规律。结果表明,在温州企业成长系统发展过程中,人才、资金、管理等因素交替成为系统发展的主导因素,促使系统不断地走向新的稳定,不断成长与发展。     

我国体育用品产业的发展路径研究——基于耗散结构视角

在我国体育用品产业不断从“被组织”向“自组织”方向发展的背景下,基于耗散结构对我国体育用品产业的发展现状及对策研究进行分析。结果显示,我国目前体育用品产业呈现供给结构不合理,科学技术水平不高,服务水平较低等问题。通过对耗散理论的研究,本研究发现我国发展体育用品产业的前提是保持开放性,必要条件是远离平衡态,内在条件是非线性相互作用,产业发展的“开关”是涨落机制。基于此,本研究拟从耗散结构的研究视角为我国体育用品产业的可持续发展提供理论建议。     

中美图情科研机构研究领域比较研究

科研机构在科学研究和技术创新过程中发挥着重要作用,对不同国家科研机构的研究领域进行分析有助于促进相互学习并提高合作的可能性。本文利用自组织映射(SOM)人工神经网络方法对87所中美图书馆学情报学(LIS)科研机构在技术维度的图书情报学领域研究进行可视化比较分析。研究发现,中美图情科研机构都与本国同行具有更相似的研究领域。根据其研究领域的相似性,共识别出八组国际潜在合作机构和七组国内潜在合作机构,最后分析了中美图情机构的热点与特色研究领域。该研究发现有助于更好地了解中美图情机构在技术维度的图书情报学研究领域的相似性与差异,为相关科研机构寻找潜在的合作对象,跟踪国外热点研究领域,发现自身研究的不足提供决策支持。图1。表6。参考文献42。附录1。     

论生态图书馆系统的自组织范式

文章对自组织科学的耗散结构论、超循环论、协同论进行了简要的介绍和分析，根据自组织理论阐释了生态图书馆系统的自组织运动方式和特征，进而构筑了生态图书馆系统的自组织范式。     

Multilingual document mining and navigation using self-organizing maps

One major approach for information finding in the WWW is to navigate through some Web directories and browse them until the goal pages were found. However, such directories are generally constructed manually and may have disadvantages of narrow coverage and inconsistency. Besides, most of existing directories provide only monolingual hierarchies that organized Web pages in terms that a user may not be familiar with. In this work, we will propose an approach that could automatically arrange multilingual Web pages into a multilingual Web directory to break the language barriers in Web navigation. In this approach, a self-organizing map is constructed to train each set of monolingual Web pages and obtain two feature maps, which reveal the relationships among Web pages and thematic keywords, respectively, for such language. We then apply a hierarchy generation process on these maps to obtain the monolingual hierarchy for these Web pages. A hierarchy alignment method is then applied on these monolingual hierarchies to discover the associations between nodes in different hierarchies. Finally, a multilingual Web directory is constructed according to such associations. We applied the proposed approach on a set of Web pages and obtained interesting result that demonstrates the feasibility of our method in multilingual Web navigation.     

自组织特征映射神经网络及其应用

介绍了自组织特征映射神经网络理论依据及其算法实现,通过应用实例的计算机仿真,表明算法的性能,并对自组织特征映射神经网络的未来发展进行了展望.