Input-to-state stability of delayed reaction-diffusion neural networks with multiple impulses

Weight learning for ℋ∞$$ {\mathscr{H}}_{\infty } $$ stabilization of uncertain switched neural networks with external disturbance and reaction‐diffusion

“…with 𝜆 max (⋅) representing the maximum eigenvalue of the matrix. Then, the  ∞ stability of SNN ( 25) is achieved if W j (t)(j = 1, … , M) is updated as (27).…”

“…With this in mind, analysis and control of dynamic reaction‐diffusion neural networks have been a topic of concern in the 21st century. For reaction‐diffusion SNNs under various switching mechanisms, there are plenty of research activities available ranging from exponential stability analysis, 26 input‐to‐state stability analysis, 27 strict passivity analysis, 28 and memory‐based state estimation, 29 to synchronization via resilient fault‐tolerant control, 30 sampled‐data control, 31 or quantized control 32 . A question remains unclear, however, is whether and how to $$$ {\mathscr{H}}_{\infty } $$$ stabilize uncertain SNNs with external disturbance and reaction‐diffusion.…”

New Results on Controllability for a Class of Fractional Integrodifferential Dynamical Systems with Delay in Banach Spaces

“…Time delay occurs frequently and is inevitable in various practical systems of the real world [15][16][17][18][19]. This is especially true for dynamical evolution processes which are closely related to time.…”

Controllability of nonlinear fractional evolution systems in Banach spaces: A survey

“…In addition, control theory of dynamical systems with impulse [7,35,47,74] or time delay [39,48,109,110] have been studied in the last decates. Some other excellent results for such systems are obtained on stability [13,33,42,43,44,61,70,105,106,126,127] and stabilization [8,34,36,46,52,82,112,113], optimal control [1,4,11,17,27,30], etc. As an important component of technology and mathematical control theory, controllability has already gained considerable attention.…”