弧焊机器人焊接咨询专家系统

为了推广弧焊机器人的应用，应用ＶｉｓｕａｌＢＡＳＩＣ和Ｃ语言开发了一个弧焊机器人焊接咨询专家系统，可用来帮助弧焊机器人操作者进行焊接工艺的制定和选择。

基于专家系统的多移动机器人模糊自校正协调系统

研究多移动机器人的运动规划问题，在实时运动规划专家系统的基础上提出了一种串级模糊控制器，以校正实际工作环境下各机器人的运动状态与理想情况下可能产生的误差，使各机器人正确调整各自运动状态，达到协调工作的目的。

分布式过程控制系统在大电流稳流控制中的应用

本文介绍了一个用于供电系统的ＤＰＣＳ，以及智能控制─实时专家系统控制在ＤＰＣＳ中的应用、特点和实现。

越野移动机器人自动驾驶专家系统的研究

本文研究越野移动机器人驾驶专家系统等有关问题．首先介绍了系统的硬件支持环境，然后阐述了自动驾驶专家系统的总体结构，有关知识库的内容以及使用知识的各功能模块的作用与运行机理．该系统已部分得以应用，能够完全代替驾驶员完成各种驾驶操作，并能进行自主导航、运动规划、自动绕障、动态跟踪目标、原路返回以及示教再现等复杂任务。

《天马》的窗口生成子系统

本文介绍了《天马》专家系统开发环境中窗口生成系统的设计原理及其实现,包括一组丰富实用的窗口操作函数及一个交互式窗口设计环境,方便了用户设计自己开发系统的人机界面。

一个数控自动编程系统的自然语言接口

本文简要介绍了一个数控自动编程专家系统的自然语言接口的实现.该自然语言接口是以我们研制的数控自动编程专家系统为背景,运行在 SUN3/4 工作站的 UNIX 下和 IBM/AT 机的 DOS 下,用 C语言编程.该自然语言接口由词法分析、句法分析、语义语用分析、目标生成和图形仿真五个模块及相应的知识库构成.该接口能够接受数控编程系统所需的对工件的英语自然语言描述并处理一些比较简单的英语语言现象.

柔性制造单元的智能调度系统

本文基于递阶控制原理提出了柔性制造单元的一种新的计划与调度方法,并综合采用了理论分析、专家系统技术和仿真技术,建立了一个智能调度系统原型.另外,在制造单元调度问题的描述上采用了状态方程形式,从系统的观点来研究调度问题.并在此基础上建立了仿真模块,对单元的加工过程进行了仿真实验.

数控自动编程专家系统

本文简要地介绍了数控自动编程专家系统.其中包括:专家系统知识表示的形式;分层次的黑板结构;前向推理求解策略和相应的解释功能;系统针对不同类型的曲线组合,采用不同的独立的知识源(KS)进行处理.由于在知识的处理上采用编码技术,在前向推理求解策略中使用启发信息和“剪技”技术,提高了系统的时空效率.系统中的规划程序能自动规划切削路径.输出供数控车床使用的 NC 代码,并可在显示屏上进行图形显示和切削仿真.目前原型系统已经在 IBM-PC 和 Sun3/60计算机上利用FORTRAN 语言实现.

知识表达语言(NFA)及其在石油测井解释专家系统中的应用

石油测井解释是一项逻辑推理和数值计算交错进行的复杂过程。为了描述测井解释专家的这种知识、经验并模拟其思维方式,在扩充纯产生式规则的基础上,我们开发了知识表达语言——NFA,它把逻辑推理和数值计算综合成统一的形式。石油测井解释专家系统 LIX 先后在 INTERDATA-85机和 PE-3230机上实现,现场(胜利油田)运行近两年,解释了130余口井,符合率94%以上。LIX 实质上是 NFA 语言的解释系统,它的研制成功,说明了 NFA 语言的有效性和实用性。

一种基于模糊petri网专家系统的面向对象方法设计

简要介绍了模糊petri网以及模糊产生式规则,给出了基于模糊petri网的专家系统的框架设计,并提出了模糊产生式规则和模糊petri网的详细设计,根据本设计方案开发了汽车变速箱故障诊断专家系统,证明设计方案简洁高效,扩充性和实用性好。

管理心理学专家系统开发工具（ESBT－MP）研制原理与方法的探索

The principle and methods to develop the expert system building tool for managerial psychology are explored and the prototype of "ESBT-MP" (The Expert System Building Tool for Managerial Psychology) in this research is made.

Computer-Based Diagnostic Systems: Computer-Based Troubleshooting

ISBN: 3-540-76198-5 (out of print)

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.