Fixed time synchronization of delayed fractional memristive Hopfield neural networks and its fractal dimension analysis

Key Points

• Achieving fixed time synchronization enhances the performance of memristive neural networks, enabling them to operate effectively under delayed conditions.
• The analysis explores the fractal dimension of these networks, providing insight into their complex dynamical systems.
• A mathematical framework underpins the synchronization process, which relies on the properties of Hopfield networks and their memristive characteristics.
• Further validation may be needed to assess performance in more complex or practical implementations of memristive systems.