Fuzzy and Neural Network Simulation of Nano-Mechanical Resonator Vibrations

This paper presents a comprehensive simulation study on the application of fuzzy logic and neural networks for modeling and controlling nano-mechanical resonator vibrations. The nonlinear dynamics of the resonator are simulated using a Duffing oscillator model with geometric nonlinearity, producing characteristic underdamped oscillatory responses. A feedforward neural network is implemented for system identification, demonstrating accurate short-term vibration prediction while revealing limitations in long-term forecasting due to error accumulation in nonlinear regimes. A Mamdani-type fuzzy logic controller is designed with displacement error and its derivative as inputs, generating an intelligent control surface that applies appropriate damping forces. The integrated simulation shows that the fuzzy controller effectively suppresses resonator vibrations when combined with the neural network's modeling capabilities. This research demonstrates that hybrid fuzzy-neural approaches provide an effective framework for managing complex nonlinear behaviors in nano-mechanical systems where traditional control methods face challenges.

Keywords:

Nano-Electromechanical Systems (NEMS) , Nonlinear Vibration , Duffing Oscillator , System Identification , Artificial Neural Network (ANN) , Fuzzy Logic Control , Intelligent Control , Hybrid Modeling