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Global Asymptotic Stability of Complex-Valued Delayed Neural Networks for Optimization Problems
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Author(s): Manoj Nagappan (SRM Institute of Science and Technology, Kattankulathur, India)and Sriraman Ramalingam (Vellore Institute of Technology, Chennai, India)
Copyright: 2026
Pages: 26
Source title:
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch007
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Abstract
In engineering sciences, the neural networks (NNs) applications related to solving optimization problems, the existence and uniqueness of stable equilibrium points for dynamical NNs is of crucial importance in the context of both theoretical and application aspects since the concerned NN may keep multiple equilibrium points that may result in some misleading suboptimal outcomes. Therefore, to solve optimization problems effectively, dynamical NNs must be designed with specific structural and dynamical properties. Recently, real-world datasets have become increasingly complex and high-dimensional, illustrating the need for efficient methods to handle processing and optimization tasks. Addressing these challenges, this chapter investigates the asymptotic stability of complex-valued neural networks (CVNNs) with time delays by employing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) approach. Finally, numerical examples and graphical illustrations are provided to validate the theoretical findings.
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