Design of neuro-fuzzy controller based on dynamic weights updating

dc.contributor.author	Hafez, Abdul
dc.contributor.author	Alrabie, Ahmed
dc.contributor.author	Agarwal, Arun
dc.date.accessioned	2022-03-27T05:52:29Z
dc.date.available	2022-03-27T05:52:29Z
dc.date.issued	2004-01-01
dc.description.abstract	Neural and fuzzy methods have been applied effectively to control system theory and system identification. This work depicts a new technique to design a real time adaptive neural controller. The learning rate of the neural controller is adjusted by fuzzy inference system. The behavior of the control signal has been generalized as the performance of the learning rate to control a DC machine. A model of DC motor was considered as the system under control. Getting a fast dynamic response, less over shoot, and little oscillations are the function control low. Simulation results have been carried at different step change in reference value and load torque. © Springer-Verlag 2004.
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). v.3356
dc.identifier.issn	03029743
dc.identifier.uri	10.1007/978-3-540-30561-3_7
dc.identifier.uri	http://link.springer.com/10.1007/978-3-540-30561-3_7
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/8531
dc.title	Design of neuro-fuzzy controller based on dynamic weights updating
dc.type	Book Series. Article
dspace.entity.type

Now showing 1 - 1 of 1