Numerical investigation and deep learning approach for fractal–fractional order dynamics of Hopfield neural network model.

This paper investigates the dynamics of Hopfield neural networks involving fractal–fractional derivatives. The incorporation of fractal–fractional derivatives in the neural network framework brings forth novel modeling capabilities, capturing nonlocal dependencies, complex scaling behaviors, and memory effects. The aim of this study is to provide a comprehensive analysis of the dynamics of Hopfield neural networks with fractal–fractional derivatives, including the existence and uniqueness of solutions, stability properties, and numerical analysis techniques. Numerical analysis techniques, including the Adams–Bashforth method, are employed to accurately simulate the fractal–fractional Hopfield neural network system. Moreover, the obtained numerical data serves as validation for developing predictions using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural network methods. The findings contribute to the advancement of both fractional calculus and neural network theory, providing valuable insights for theoretical investigations and practical applications in complex systems analysis. • Examined 3D Hopfield network stability under fractal–fractional derivatives. • Computed solutions for diverse fractal and fractional dimensions. • Utilized LSTM and MLP to study numerical data. • Visualized Deep Learning vs. numerical results. • Employed Deep Learning to predict system solutions. [ABSTRACT FROM AUTHOR]