Uniform Stability of Complex-Valued Neural Networks of Fractional Order With Linear Impulses and Fixed Time Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 10; pp. 5321 - 5331
• Main Authors: Li, Hui, Kao, Yonggui, Bao, Haibo, Chen, Yangquan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biological neural networks; Complex-valued neural network (CVNN); Delay effects; Delays; Fixed points (mathematics); fractional order (FO); Impulses; impulsive effects; Lyapunov methods; Neural networks; Neurons; Stability criteria; time delays; Timing; uniform stability
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2021.3070136

As a generation of the real-valued neural network (RVNN), complex-valued neural network (CVNN) is based on the complex-valued (CV) parameters and variables. The fractional-order (FO) CVNN with linear impulses and fixed time delays is discussed. By using the sign function, the Banach fixed point theorem, and two classes of activation functions, some criteria of uniform stability for the solution and existence and uniqueness for equilibrium solution are derived. Finally, three experimental simulations are presented to illustrate the correctness and effectiveness of the obtained results.