Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

Choosing knowledge management strategies by using a combined ANP and DEMATEL approach

Numerous companies are expecting their knowledge management (KM) to be performed effectively in order to leverage and transform the knowledge into competitive advantages. However, here raises a critical issue of how companies can better evaluate and select a favorable KM strategy prior to a successful KM implementation. The KM strategy selection is a kind of multiple criteria decision-making (MCDM) problem, which requires considering a large number of complex factors as multiple evaluation criteria. A robust MCDM method should consider the interactions among criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with all kinds of interactions systematically. Moreover, the Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a visual structural model, but also can be used as a way to handle the inner dependences within a set of criteria. Hence, this paper proposes an effective solution based on a combined ANP and DEMATEL approach to help companies that need to evaluate and select KM strategies. Additionally, an empirical study is presented to illustrate the application of the proposed method.     

数字气田的智能信息采集与处理技术

提出基于智能信息推拉的智能信息采集与传输技术和基于公共知识库的智能通信技术。通过信息加工,提取有用的信息和知识直接传递给有需求的用户。各公共知识库之间可以共享知识,只传输不同于公共知识库已有内容的新信息,可以减少需要传输的信息量,提高通信速度。实验验证了该方法的可行性。     

从知识的表达和运用综述强化学习研究

为推进强化学习研究的进一步深入和扩大其实际应用范围,从强化学习研究的理论基础--知识袭示和运用的角度对强化学习进行分类.并就经典随机强化学习、模糊强化学习、定性强化学习以及灰色强化学习作了较详细的探讨与比较.最后从知识表达和运用的角庹对强化学习的发展进行了展望.     

基于近似匹配的知识化制造系统自重构研究

为解决实际中不同制造模式的实现之间存在大量相似但又不完全相同的模块的自重构问题,提出基于近似匹配的知识化制造系统自重构的理论和算法.首先讨论知识化制造系统自重构中知识点的匹配度定义和性质;然后给出基于近似匹配的知识网自重构算法,并利用C#和SQL Sever 2000在.NET平台上开发出基于近似匹配的自重构使能工具;最后通过实例说明基于近似匹配的自重构技术的应用.     

面向业务问题求解的知识供应过程模型

为了满足企业在业务问题求解方面的实际需求,提出了一个面向业务问题求解的知识供应过程模型。该模型提出了知识供应过程中的问题空间、知识空间和流程空间的概念及其关联关系,并进一步给出了包含知识的获取、评价、存储和重用的知识供应过程的有关算法。该模型与算法将业务问题求解过程与知识供应过程进行了融合,通过关联关系实现了知识的主动供应,在优化了业务问题求解过程的同时,提高了知识积累的质量和知识重用的效率。基于该模型建立了知识管理问题求解的原型系统KMPSS,并得到了初步应用验证。     

基于本体的个性化知识服务系统的构建

为用户提供有效的个性化知识服务是图书馆等机构信息服务发展方向之一.引入本体理论,构建了一个基于本体的个性化知识服务系统.系统利用本体对多领域、跨数据库的文献库进行统一描述和重构,在此基础上进一步提出了用户个性特征模型和个性化知识检索模型,使系统实现在多文献库间进行语义层次上的个性化检索,提高了知识服务的效率和质量.     

模糊知识粒度计算与应用

根据自反模糊关系,将知识粒度的概念推广为模糊知识粒度.考虑传统模糊粗糙集的粗糙性度量和相似性度量,忽略了模糊集的粗糙近似处于不同知识粒度背景中这样一个重要因素,结合模糊知识粒度的计算,提出了模糊粗糙集的粗糙性度量和相似性度量的新方法.最后,在一个实际的模糊信息系统中,给出了基于模糊知识粒度的知识约简算法.     

面向Web结构化信息处理的汉语知识库构建研究

对Web结构化汉语信息处理中的知识需求进行了分析,介绍了目前有影响的汉语语义资源和本体知识,给出了面向Web结构化信息处理的汉语知识库组成模型及构建方法,并在Deep Web研究中对该模型进行了应用验证.该研究旨在使计算机更全面有效地对特定领域的web结构化信息进行处理,对本体的深入研究也有一定的参考价值.     

一种基于相对粒度的决策表约简算法

在知识粒度的基础上,针对决策表提出了相对粒度和属性相对重要性的概念,证明了知识的相对粒度随着知识粒度的增大而单调增加的变化规律,在此基础上提出了一种基于相对粒度的启发式约简算法,以弥补基于正区域的约简方法处理不一致决策表时存在的不足.通过理论分析和实例验证表明,该算法是有效的,且其时间复杂度相对较低.