Data-driven learning of fractional difference equations is investigated in this paper. Firstly, a
multi-layer neural network is designed. Loss functions are constructed by use of the …

The nonlinear behavior of the airfoil-store system introduces complexity to its mechanical
characteristics, making it essential to identify the system's nonlinear features. This study …

To solve the performance degradation of chaotic system caused by computational precision
limitation, a new hybrid control method is proposed by the Parameter Perturbation and …

Parameter estimation is important in data-driven fractional chaotic systems. Less work has
been reported due to challenges in discretization of fractional calculus operators. In this …

This exhaustive study entails fractional processing of the unified chaotic Lorenz-Lü-Chen
attractors using machine learning expedition with Levenberg-Marquardt optimized deep …

This paper proposes an adaptive integral alternating minimization method (AIAMM) for
learning nonlinear dynamical systems using highly corrupted measured data. This approach …

For the fixed-time nonlinear system control problem, a new fixed-time stability (FxTS)
theorem and an integral sliding mode surface are proposed to balance the control speed …

Data-driven learning of the fractional discrete-time unified system is studied in this paper. A
neural network method is suggested in the parameter estimation of fractional discrete-time …

In the computation of dynamic response sensitivity for rotor-bearing systems using the
multicomplex variable derivation method, the presence of nonlinearity, particularly “fractional …

We study the accuracy of machine learning methods for inferring the parameters of noisy
fractional Wu-Baleanu trajectories with some missing initial terms. Our model is based on a …