Web农业专家系统多媒体技术的应用研究

多媒体技术为专家系统多媒体知识的表达提供了良好的技术支持，多媒体知识是专家系统启发式主流知识表示的有益补充。本文结合Web油菜优质高产高效生产专家系统的开发实例，运用ASP技术和DCOM技术很好地实现了在产生式知识规则的前件和后件中多媒体知识的融合及其控制，并实现了多媒体程序与分布式专家系统平台的无缝集成。     

知识表示在牡丹栽培技术专家系统中的应用

介绍了协同式专家系统的概念和基于关系的知识表示的原理，详细分析了牡丹栽培技术专家系统中用于知识表示的关系模式，提出了一种协同式专家系统的设计方案。     

运用Web Service技术构建专家系统之间的交互

专家系统解决问题的范围常常受到知识领域狭窄的限制。Web Service实现了“基于Web无缝集成”的目标，可以运用Web Service技术来实现专家系统之间的交互，弥补专家系统知识不足的问题。文中运用Web Service提供的技术构建网络上专家系统之间的交互，并且对这种交互的可行性进行了分析研究，这就不仅为解决专家系统知识不足的问题提供了方法和技术，而且进一步构建了一个模型，使得网络上专家系统互相协作，共同解决一个领域更广的问题。     

一个基于神经网络的智能评价决策专家系统

本文在研究现代智能评价决策专家系统和神经网络的基础上，提出了一种基于神经网络的智能评价决策专家系统，它既能保持专家系统原有特色，又兼有神经网络特点，可以同时获得问题领域中的规范性知识和经验性知识，在实际应用中取得了令人满意的结果。     

专家系统中不确定性知识的表示和处理

知识表示和处理是专家系统的基本问题，不确定性知识的表示和处理一直是专家系统研究的热点。本文在阐述不确定性知识概念的基础上，简单介绍传统的不确定知识表示和处理方法，重点讨论近年来出现的新的不确定知识表示和处理方法。     

基于神经网络专家系统的电机智能CAD研究

应用专家系统进行电机智能ＣＡＤ，存在着知识获取瓶颈和推理过程中的匹配冲突、组合爆炸、无穷递归等问题。本文研究了神经网络专家系统的基本原理，即知识的神经网络表示方法、推理机制和知识获取。针对电机设计的特点，提出了一种智能ＣＡＤ神经网络专家系统的结构。     

专家系统在质量检测中的应用

本文通过专家系统在化工产品质量检测中的应用，介绍知识工程的原理，专家系统中知识表示的方法，模糊评判，归纳分类，知识获取的途径。     

基于Rough Set的规则自动抽取设计方案

知识获取是专家系统的重要研究领域，而Rough Set理论以理论的独特之处成为这一领域的有效工具。文章针对一具体专家系统－OTCA－ES专家系统－在知识获取方面能力的不足，简要介绍其知识表示和知识获取的方法后，提出了一种基于Rough Set理论的规则自动抽取的设计方案。     

纺织专家系统中知识与知识库的应用

专家系统是人工智能的一个分支，是一种模拟专家决策能力的计算机系统，知识库是系统的重要组成部分，是系统的核心。本文结合纺织工艺设计及管理专家系统介绍了基于知识的专家系统的概念和结构，对系统中知识的获取、存储方式予以说明．并对其知识的表示方法加以阐述。     

故障诊断专家系统的归纳学习方法

本文对故障诊断专家系统实际运行的实例进行旭纳分析，以求发现新的知识或修改原有知识库的知识，来提高专家系统的适应性和运行质量。     

Computer networks design using hybrid fuzzy expert systems

 Designing and configuring large computer networks to support a variety of applications and computational environments is difficult, as it not only requires highly specialized technical skills and knowledge, but also a deep understanding of a dynamic commercial market. Hybrid fuzzy expert systems integrate fuzzy expert systems and neural networks methods replacing classical hard decision methods and providing better performance than traditional techniques. In this paper, we present an integrated fuzzy expert system, machine learning, and neural networks approach to large structured computer networks design and evaluation. After presenting an overview of the system and the major research choices, we describe in detail the system's modules and present examples of its potential use.     

Opening the black box of neural networks for remote sensing image classification

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classification decisions explicitly in the form of fuzzy ‘if-then’ rules. However, the construction of a knowledge base, especially the fine-tuning of the fuzzy set parameters of the fuzzy rules in a fuzzy expert system, is a tedious and subjective process. This research has developed a new, improved neuro-fuzzy image classification system based on the synergism between neural networks and fuzzy expert systems. It incorporates the best of both technologies and compensates for the shortcomings of each. The learning algorithms of neural networks developed here are used to automate the derivation of fuzzy set parameters for the fuzzy ‘if-then’ rules in a fuzzy expert system. The rules obtained, in symbolic form, facilitate the understanding of the neural network based image classification system. In addition, the image classification accuracy obtained from the improved neuro-fuzzy system was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.     

系统仿真的新技术探讨－（3）：人工智能

系统仿真技术近几十年得到了很快地发展，已广泛运用于许多领域。现有的系统对数值模型的仿真研究得较深入。但对认知模型和感知模型的仿真（模拟人的思维、问题求解、外界感知的能力），现有的系统对此研究得较少。本文探讨系统仿真的新技术之一：人工智能，主要介绍专家系统、人工神经网、模糊系统在系统仿真中的应用。     

基于模糊数据挖掘和遗传算法的网络入侵检测技术

文章通过开发一套新的网络入侵检测系统来证实应用模糊逻辑和遗传算法的数据挖掘技术的有效性;这个系统联合了基于模糊数据挖掘技术的异常检测和基于专家系统的滥用检测,在开发异常检测的部分时,利用模糊数据挖掘技术来从正常的行为存储模式中寻找差异,遗传算法用来调整模糊隶属函数和选择一个合适的特征集合,滥用检测部分用于寻找先前行为描述模式,这种模式很可能预示着入侵,网络的通信量和系统的审计数据被用做两个元件的输入;此系统的系统结构既支持异常检测又支持滥用检测、既适用于个人工作站又可以适用于复杂网络。     

基于人工神经元网络的控制系统模型简化的专家系统

本文研究并实现了一个基于人工神经元网络的控制系统模型简化的专家系统(简称为ESOMRT)。该系统适用于专家和非专家用户,能够针对更体的连续和离散时间的高阶控制系统模型和简化要求选择合适的简化方法,并可对简化质量从时域和频域方面进行评估。在构造这个系统的过程中,作者提出了智能数据库的概念,使用了过程型和人工神经元网络方法相结合的知识表达方式,并利用神经元网络的再学习机制实现了斗自动知识获取,该系统具有三种工作模式和友好的人机界面,使系统的智能水平比较高并有实用价值,现已在IBM-PC/XT和386机上运行。     

复杂控制系统智能化建模方法及其应用

本文针对自动控制系统中普遍存在的复杂性和不确定性,为弥补传统的基于微分方程、传递函数、状态方程的建模方法的不足,将人工智能思想引入自动控制系统的建模之中,提出了基于谓词形式的自动控制系统智能语言描述方法,采用这种方法,描述自动控制系统简单方便,不仅可以描述常规的控制方法,而且可以描述专家控制、模糊控制和基于神经网络的控制等各种智能控制方法,为分析复杂系统、研究智能控制系统提供了方便。     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

An adaptive neuro-fuzzy model for prediction of student’s academic performance

This paper introduces a systematic approach for the design of a fuzzy inference system based on a class of neural networks to assess the students’ academic performance. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, there is an increasing trend to expand them with learning and adaptation capabilities through combinations with other techniques. Fuzzy systems-neural networks and fuzzy systems-genetic algorithms are the most successful applications of soft computing techniques with hybrid characteristics and learning capabilities. The developed method uses a fuzzy system augmented by neural networks to enhance some of its characteristics like flexibility, speed, and adaptability, which is called the adaptive neuro-fuzzy inference system (ANFIS). New trends in soft computing techniques, their applications, model development of fuzzy systems, integration, hybridization and adaptation are also introduced. The parameters set to facilitate the hybrid learning rules for the constitution of the Sugeno-type ANFIS architecture is then elaborated. The method can produce crisp numerical outcomes to predict the student’s academic performance (SAP). It also provides an alternative solution to deal with imprecise data. The results of the ANFIS model are as robust as those of the statistical methods, yet they encourage a more natural way to interpret the student’s outcomes.     

基于模糊神经网络的单兵装备效能评估专家系统

本文提出了一种客观评价单兵装备效能的方法,在模糊神经网络算法的基础上开发了一套专家系统。对测得的样本数据进行实验分析,证明此系统具有推理效率及准确性较高的特点。     

Applications of a universal expert system in industry

The theory of artificial intelligence has reached a level which enables practical applications. As a consequence of this several expert systems (corrosion, rubber science etc.) already exist. However, a development of such expert systems is time-consuming and requires highly sophisticated software. Therefore a fuzzy based expert system SENECA is proposed. A fuzzy simulation program is available so it is relatively easy to develop the expert system based on a concept of fuzzy similarity. An expert base of SENECA can absorb very heterogenous, partially inconsistent and incomplete data of different range and accuracy. The application of SENECA is demonstrated on a hypothetical chemical reactor with two independent variables.