DocumentCode :
293638
Title :
A neurocomputer based learning controller for critical industrial applications
Author :
Shukla, K.K.
Author_Institution :
Dept. of Comput. Eng., Banaras Hindu Univ., Varanasi, India
fYear :
1995
fDate :
5-7Jan 1995
Firstpage :
43
Lastpage :
48
Abstract :
Because of their remarkable ability to learn nonlinear mappings from a nonexhaustive training set and generalize, neurocomputers can readily play the role of learning controllers for complex plants. Due to the parallel distributed nature of processing they introduce little time delay in the control loop. In this paper a new generalized feedforward neural network model is applied to the task of controlling an aircraft engine model. A more effective learning algorithm based on adaptive optimization is presented which compares favorably with the classical backpropagation method. Similar controllers can be designed for critical industrial applications such as nuclear power plants etc
Keywords :
aerospace engines; feedforward neural nets; learning (artificial intelligence); learning systems; neurocontrollers; adaptive optimization; aircraft engine model; complex plants; critical industrial applications; generalized feedforward neural network model; learning algorithm; learning controllers; neurocomputer based learning controller; nonexhaustive training set; nonlinear mappings; Aircraft propulsion; Application software; Artificial neural networks; Automatic control; Control systems; Digital control; Electrical equipment industry; Industrial control; Industrial training; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Automation and Control, 1995 (I A & C'95), IEEE/IAS International Conference on (Cat. No.95TH8005)
Conference_Location :
Hyderabad
Print_ISBN :
0-7803-2081-6
Type :
conf
DOI :
10.1109/IACC.1995.465870
Filename :
465870
Link To Document :
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