基于专家系统技术的网络计划工期估算研究

本文主要探讨了专家系统技术在网络计划工作估算中的应用.首先分析了网络计划的领域知识,并采用面向对象的方法进行知识表示;在推理机制中讨论了在面向对象知识表示情况下的推理流程.最后对ESDENP的结构和主要功能作了介绍.     

结合面向对象方法的计算机视觉语义网络表示

本文从理论上分析一种新的计算机视觉关系模型表示方法：面向对象语义网络。这种表示方法减少了节点与语义联系的种类和数量,简化了表示形式。在面向对象语义网络的类里特别引进了属性的缺省值,在推理中引入缺省推理机制,这种方法符合人类思维方式,在类和对象中引进了操作,可以通过现有的知识推理新的知识,在属性中引进运动属性知识,使得这种语义网络更加有效地表达运动的场景。     

智能神经网络系统组成原理在语音识别中的应用

在语音识别实验中,应用智能神经网络系统组成原理,研究了以简单、功能单一的网络为基础,构造复杂、功能完善的智能神经网络系统的方法,同时,通过加入规则,提高了网络的知识能力,实验表明,智能神经网络系统组成原理将面向对象、符号逻辑融于神经网络中,提供了构造功能完备的智能系统的途径。     

故障决策树模型在诊断专家系统中的应用

从通信装备的故障实例出发,将故障决策树应用于诊断专家系统,并采用面向对象技术实现了基于故障决策树模型的知识表示与推理机制.     

对一种贝叶斯网络学习算法的改进及试验分析

贝叶斯网络是上世纪80年代发展起来的一种概率图形模型,它提供了不确定性环境下的知识表示、推理、学习手段,可以完成决策、诊断、预测、分类等任务,已广泛应用于数据挖掘、语音识别、工业控制、经济预测、医疗诊断等诸多领域。然而由于贝叶斯网络的推理和贝叶斯网络的学习问题都是NP难的,其实际应用受到很大限制。贝叶斯网络推理是利用它进行决策、诊断、分类、预测等应用的基础,其本质任务是计算边缘概率分布。当网络比较复杂时,推理将变得不可行。多模块的贝叶斯网络(MSBN)从简化模型本身出发,对贝叶斯网络进行了扩展。我们则提出了一种用于MSBN中的近似推理算法,这些都大大拓宽了贝叶斯网络的应用领域。     

医疗诊断专家系统推理机的设计与实现

专家系统是人工智能领域的重要分支,推理机是专家系统的重要组成部分。文中用关系数据库SQLServer2000设计诊断知识库,构造了一个医疗诊断专家系统,其推理机能够有效模拟医生的诊断思维。因此,它可以作为医生诊断疾病的一种辅助工具。根据疾病诊断的要求,仅对医疗诊断推理机的设计与实现方法进行了探讨,提出了一种基于正-反向推理的精确与不精确推理相结合的推理策略,并采用关系数据库和面向对象的技术来具体编程实现,从而提高了推理机的效率,获得了较好的推理效果。     

一种新的面向对象的概率图模型

针对大规模Bayes网络的知识表示和推理等问题,使用面向对象的方法扩展Bayes网络结构,提出了一种新的概率图模型——对象概率模型（OPM）．该模型充分利用层次结构中所蕴含的条件独立性,有效地降低了知识表示的复杂度．在Bayes网络消元推理算法的基础上设计了OPM的一种有效的推理算法,该算法可以根据需要调节推理的计算量,在一定程度上解决了概率推理的计算的复杂度问题．将OPM用于解决图像中文本的自动检测与定位问题,实验结果验证了模型的有效性．     

网络故障管理专家系统中知识库的构造

故障管理在网络管理中占有重要地位.知识库是专家系统的核心组成部分.本文针对网络告警关联规则专家系统,构造了一种面向对象知识库的体系结构,介绍了采用面向对象技术对网络中的事实型知识和关联规则进行表示的方法,并设计了在此基础上的推理方案.     

医疗诊断专家系统推理机的设计与实现

专家系统是人工智能领域的重要分支,推理机是专家系统的重要组成部分.文中用关系数据库SQL Server2000设计诊断知识库,构造了一个医疗诊断专家系统,其推理机能够有效模拟医生的诊断思维.因此,它可以作为医生诊断疾病的一种辅助工具.根据疾病诊断的要求,仅对医疗诊断推理机的设计与实现方法进行了探讨,提出了一种基于正-反向推理的精确与不精确推理相结合的推理策略,并采用关系数据库和面向对象的技术来具体编程实现,从而提高了推理机的效率,获得了较好的推理效果.     

产品概念创新设计系统--CIDS

介绍开发的概念创新设计原型系统CIDS的知识库设计以及知识推理部分的算法．知识库利用框架结构进行面向对象的建模，描述实例知识功能特征和结构特征；知识推理部分将实例推理和规则推理结合使用以提高推理能力．提出一种改进的计算相似度的算法．介绍了使用CIDS进行手机产品概念设计的主要步骤．     

医疗诊断专家系统推理机的AM网络设计

本文探讨了医疗诊断专家系统（ＭＤＥＳ）的特殊性，提出了适合于ＭＤＥＳ推理机的联想记忆神经网络（ＡＭＮＮ）。利用该网络模型不要求样本独立和二值输入等特性，引入隶属度，将模糊模式识别运用于ＭＤＥＳ《     

Using augmenting modular neural networks to evolve neuro-controllers for a team of underwater vehicles

The paper presents a new generative neuro-evolutionary method called augmenting modular neural networks (AMNN). As the name of the method implies, its purpose is to construct neural networks with a modular architecture. In addition to the modularity itself, neural networks evolving according to AMNN are also characterized by gradually expanding architecture. In the beginning of the evolutionary process, all networks consist of only output modules (or a single module). After some time, if the architecture of all networks is insufficient to effectively perform a task, all of them are augmented by one hidden module. In the following generations, further hidden modules are also added and this procedure is continued until some stopping criterion is satisfied. To test performance of AMNN, the method was used to evolve neuro-controllers for a team of underwater vehicles whose common goal was to capture other vehicle behaving by a deterministic strategy (predator–prey problem). The experiments were carried out in simulation, whereas their results were used to compare AMNN with neuro-evolutionary methods designed for building monolithic neural networks.     

一种基于AOP的企业应用开发技术

面向方面的编程(Aspect-OrientedProgramming,AOP)是一种新的编程技术,它弥补了面向对象的编程(Object-OrientedProgramming,OOP)在跨越模块行为上的不足。AOP引进了Aspect,它将影响多个类的行为封装到一个可重用模块中,它允许程序员对横切关注点(crosscuttingconcerns,即跨越典型职责界限的行为,例如日志记录)进行模块化,从而消除了OOP引起的代码混乱和分散问题,增强了系统的可维护性和代码的重用性。该文首先分析了AOP与OOP的比较,然后给出了AOP的原理和AOP的JBoss实现,最后介绍AOP在设计模式中的应用,并说明了使用它所带来的设计益处以及在应用AOP中应注意的问题。     

协议状态机推断技术研究进展

介绍了协议逆向工程中协议状态机推断的研究进展.从状态机推断与正则语言学习的关系出发,分析了协议状态机推断的目标与需求;然后依据分析对象不同,将协议状态机推断分为基于指令序列和报文序列两大类,进一步将基于报文序列的状态机推断分为主动推断和被动推断两种;研究分析了各层面涉及到的方法与技术,并作出了评价与比较.最后对协议状态机推断研究的发展趋势进行了展望.     

并行知识库机的知识划分及其推理

并行知识库机是一种采用并行处理技术来提高大规模知识库系统性能的专用机。其中，知识的划分及并行推理模型是两个关键问题。本文以产生式系统作为并行知识库机的基本知识表示方法，重点讨论产生式系统的划分和并行推理问题。     

HELIC-II: Legal reasoning system on the parallel inference machine

This paper presents HELIC-II, a legal reasoning system on the parallel inference machine. HELIC-II draws legal conclusions for a given case by referring to a statutory law (legal rules) and judicial precedents (old cases). This system consists of two inference engines. The rule-based engine draws legal consequences logically by using legal rules. The case-based engine generates legal concepts by referencing similar old cases. These engines complementally draw all possible conclusions, and output them in the form of inference trees. Users can use these trees as material to construct arguments in a legal suit. HELIC-II is implemented on the parallel inference machine, and it can draw conclusions quickly by parallel inference. As an example, a legal inference system for the Penal Code is introduced, and the effectiveness of the legal reasoning and parallel inference model is shown.     

Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly

This paper presents a methodology for generating a fuzzy inference mechanism (FIM) for recognizing contact states within robotic part mating using active compliant motion. In the part mating process, significant uncertainties are inherently present. As a result it is pertinent that contact states recognition systems operating in such environment be able to make decisions on the contact state currently present in the process, based on data full of uncertainties and imprecision. In such conditions, implementation of fuzzy logic and interval inference brings significant robustness to the system. As a starting point for FIM generation, we use a quasi-static model of the mating force between objects. By applying Discrete Wavelet Transform to the signal generated using this model, we extract qualitative and representative features for classification into contact states. Thus, the obtained patterns are optimally classified using support vector machines (SVM). We exploit the equivalence of SVM and Takagi–Sugeno fuzzy rules based systems for generation of FIM for classification into contact states. In this way, crisp granulation of the feature space obtained using SVM is replaced by optimal fuzzy granulation and robustness of the recognition system is significantly increased. The information machine for contact states recognition that is designed using the given methodology simultaneously uses the advantages of creation of machine based on the process model and the advantages of application of FIM. Unlike the common methods, our approach for creating a knowledge base for the inference machine is neither heuristic, intuitive nor empirical. The proposed methodology was elaborated and experimentally tested using an example of a cylindrical peg in hole as a typical benchmark test.     

面向方面的编程的研究与实现

面向方面的编程(AOP）是一种新的编程技术,它弥补了面向对象的编程(OOP)在跨越模块行为上的不足。 AOP 引进了Aspect,它将影响多个类的行为封装到一个可重用模块中,它允许程序员对横切关注点进行模块化,从而消除了OOP引起的代码混乱和分散问题,增强了系统的可维护性和代码的重用性。首先进行了AOP与OOP的比较,给出了AOP的原理和AOP的JBoss实现,最后介绍AOP在设计模式中的应用,并说明了使用它所带来的设计益处以及在应用AOP中应注意的问题。     

An Introduction to Variational Methods for Graphical Models

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.