Artificial Neural Network Model of Nonlinear Behavior of Micro-ring Gyroscopes

The investigation is concerned with developing a neural network tomodel a micro-ring gyroscope considering the exact term for the electrical force. To this end, Hamilton’s principle alongside Ritz’s method has been utilized to obtain the non-linear system of equations governing the dynamics of the micro-ring. The equations are then numerically solved using the fourth-order Runge-Kutta method. It has been observed considering the Taylor series expansion of the electrical force may lead to misleading results in the case of large deformations. Furthermore, gathering a dataset from numerical solutions induces high computational costs and time. So, a fast method is required to obtain a sizable dataset with good accuracy. To this end, the system has been modelled with an artificial neural network. To form the neural network 1720 examples have been gathered with five input features. It is shown that the proposed neural network can perfectly predict the behavior of the micro-ring gyroscope.