Combining response surface optimization and stochastic analysis for crashworthiness design an introductory study

the active noise control (ANC) is discussed. Many digital NC systems often based on the filter-x algorithm lor hnite impulse - response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control systems based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise leedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. n on-line- learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.