Global Stability and Synchronization of Complex‐Valued Differential‐Algebraic Neural Networks With Delay: A Differential‐Algebraic Inequality Approach
Weiqiang Gong, Yao Xiao, Kai Wang, Qiang Li
Source abstract
ABSTRACT This article proposes a novel complex‐valued differential‐algebraic neural network model with time delay (DDANN). Firstly, based on the differential‐algebraic inequality technique, the global stability criteria of the considered system are obtained to ensure the complex‐valued DDANN achieves global exponential stability. Next, by designing an appropriate feedback controller, to get the desired results, we present some constraint conditions, which make the drive system and the response system achieve the global exponential synchronization. Furthermore, as an application, a complex‐valued neutral‐type neural network (NDNN) can be transformed into a simple complex‐valued DDANN and the global exponential stability results of NDNN are also proposed. In the end of paper, two examples are given to verify the superiority/feasibility of the presented theoretical results.
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