一种适合于网络专家系统的通用理论(英文)

General framework for building networked expert systems

描述运用于工业自动化的一种自适应自学习方法。基于此方法，Ａｐｔｒｏｎｉｘ公司开发出一套通用软件工具－ＳＴＩＭＴＭ，并应用于不同工业领域。ＳＴＩＭ可用于构造各类专家系统。基于因素空间理论，ＳＴＩＭ具有一系列独特之处，比如自动化、自学习，以及自翻译。如果与因特网，嵌入式控制器以及可编程逻辑控制器等结合使用，ＳＴＩＭ则成为一个十分有效的工具，可应用于远程连通与控制、模式识别、机器故障诊断。

Based on the factor space theory, a general framework called the expert machine, for building fuzzy expert systems is introduced. This machine helps experts capture intuitive expertise without the assistance of knowledge engineers, automatically builds knowledge base, simplifies knowledge representation, reduces computer storage, and provides high-speed inference suitable for on-line problem solving,especially for networked diagnosis and data fusion (object detection). As an expert shell, it provides an innovative method for knowledge representation different from most current fuzzy logic-based expert systems that rely on ad hoc membership functions obtained from intuition and experience. A high-speed synthetic reasoning algorithm is provided so that the inference process is computationally efficient. It acts like a configurable blackboard that converts raw input data from experts and compiles them into a source data base, which is down-loaded into a TVFI (truth value flow inference) module. This technique presents great potential applications in both military and commercial systems.