Fault-tolerant output-feedback stabilization for complex-valued neural networks with time delay and actuator failures
Source abstract
Abstract
The paper considers the issue of fault-tolerant output-feedback stabilization for complex-valued neural networks with both time delay and actuator failures. The aim is to design a fault-tolerant output-feedback controller to ensure the network to be asymptotically stable. By using the discretized Lyapunov-Krasovskii functional method as well as the free-weighting matrix approach, a delay-dependent stability criterion is proposed. Then, with the aid of some decoupling techniques, a method for the design of desired output-feedback fault-tolerant controller is developed. Finally, a numerical example is given to verify the effectiveness of the present stabilizing method.
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Research context
OpenAlex topics: Neural Networks Stability and Synchronization · Advanced Memory and Neural Computing · Neural Networks and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science & Technology, Anhui University of Technology 1 , Ma’anshan 243032, China
School of Mathematics Science, Liaocheng University 2 , Liaocheng 252000, China
College of Mathematics & Systems Science, Shandong University of Science & Technology 3 , Qingdao 266590, China
Funding
Excellent Youth Talent Support Program of Universities in Anhui Province · National Natural Science Foundation of China · Natural Science Foundation of the Anhui Higher Education Institutions
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