EVENT-BASED INTELLIGENT CONTROL USING ENDOMORPHIC NEURAL NETWORK MODEL
作者:
SUNGHOON JUNG,
TAGGON KIM,
KYUHO PARK,
期刊:
Applied Artificial Intelligence
(Taylor Available online 1995)
卷期:
Volume 9,
issue 5
页码: 479-494
ISSN:0883-9514
年代: 1995
DOI:10.1080/08839519508945486
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
In event-based control, a controller checks the responses of sensors about commands with time constraints. To do this, the event-based controller should have some information about the dynamics of the plant at discrete levels, its desired state transitions, and inputs to move the state transitions. In an existing modelling method, the information is represented by a tabular form, which is not adaptable to the variation of set positions. An artificial neural network was taken as a new modelling method to solve this problem. Experiments show that this neural network model works well in the dynamic variation of set positions. This endomorphic neural network modelling helps us to construct a more autonomous event-based controller.
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