A practical guide to designing expert systems
A practical guide to designing expert systems
A guide to expert systems
Building expert systems
Introduction to expert systems
Introduction to expert systems
Expert systems
Applications of the negation operator in fuzzy production rules
Fuzzy Sets and Systems
Artificial Intelligence and the Design of Expert Systems
Artificial Intelligence and the Design of Expert Systems
Expert Systems
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The FAULT FINDER Expert System implements fault isolation decisions for any target system or equipment that can be modeled by lowest replaceable units (hereafter called LRUs). The term “Target System” will be used to refer to the system being fault isolated. The Fault Finder expert system fault isolates the target system's LRUs. This expert system utilizes a data base to represent each LRU, a status interface to obtain LRU status, and a knowledge base to store the rules of fault isolation for the target system. The expert system has multiple “learning” capabilities in the data base, the knowledge base and the inference procedure. Another aspect of learning which influences the structure of the knowledge base is that each rule has parameters associated with it to store the information learned as a result of user feedback and the inference process. The certainty or possibility associated with the conclusion of each rule is adjusted as the system runs and gains experience. The inference procedure uses fuzzy logic for premise matching certainty, and combining of premise certainties for the rule firing certainty. This expert system brings together for the first time a fault isolation system with unique knowledge representation, inference processing, fuzzy logic, and multiple learning capabilities in one design. Also presented are issues of knowledge structure, and possible types of fault isolation knowledge.