Neural computing for numeric-to-symbolic conversion in control systems
- 1 April 1989
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems Magazine
- Vol. 9 (3) , 44-52
- https://doi.org/10.1109/37.24811
Abstract
A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.Keywords
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