NDLI: An Algorithm of Finite State Machine Based on Inexact Inference Expert System

Content Provider	IEEE Xplore Digital Library
Author	Li Tang Jing Fu Jinmin Chen
Copyright Year	2010
Abstract	This paper introduces a new type of finite state machine (FSM) - The FSM based on inexact inference expert system. The paper analyzes the uncertainty of issues of the classic FSM which is major difficulties of the classic FSM, and gives an algorithm of inexact inference in detail according to the uncertainty issues analysis. This new type of FSM based on the algorithm can handle most of uncertainty issues very well. Due to the combination of inexact inference and expert system, this new type of FSM also possesses the powerful reasoning capability and the fast state transition speed, especially for a huge logical reasoning calculation besides handling the uncertainty issues. So the FSM based on inexact inference expert system has more advantages than the classic FSM: The FSM can handle the uncertainty, the calculation speed is very fast, the state transition is intelligent, the system architecture is simple, and the system is easy to be expanded. The real world model and virtual state transition world model can be separated, so the cycle of software development will be decreased greatly. The paper gives the design strategy for FSM based on inexact inference expert system as well.
Starting Page	424
Ending Page	428
File Size	255803
Page Count	5
File Format	PDF
ISBN	9781424460793
e-ISBN	9781424460809
DOI	10.1109/ICCEA.2010.232
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-19
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Uncertainty Automation Industrial control Finite state machine Expert system Communication industry Automata Computer applications Inexact inference Inference algorithms Electrical equipment industry Expert systems
Content Type	Text
Resource Type	Article

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