Abstract
The small gain theorem is used to consider the stability of a neural network controlled system under the condition that some of the neurons may fail with attenuated outputs and possibly with the addition of a constant output bias. Sufficient conditions based on the small gain theorem and the circle criterion are obtained for fail-safe stability. A loop-transformation technique is used to overcome the conservative nature of the small gain approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
