Adaptive Inverse Vibration Control Technology Based on Neural Network Online Identification

Adaptive inverse vibration control technology based on neural network online identification represents an advanced and highly effective control methodology applicable to various nonlinear systems. This technique leverages neural networks' powerful capabilities for real-time system state identification, enabling adaptive adjustments based on identification results to achieve inverse vibration control. The implementation typically involves continuous parameter updates through backpropagation algorithms and employs inverse model compensation to counteract system oscillations.

This control approach significantly enhances system stability and response speed while effectively suppressing vibrations, allowing systems to better adapt to environmental changes and external disturbances. Key algorithmic components include recursive weight updates using gradient descent optimization and real-time error minimization between predicted and actual system outputs. With broad application prospects, this technology has demonstrated remarkable achievements in both scientific research and engineering practices, particularly in applications requiring dynamic adaptation to nonlinear behaviors.