Nonlinear System Identification Using Audio-Inspired WaveNet Deep Neural Networks

Abstract

Nonlinear system identification is a challenging task that requires accurate estimation of the structural model from observations of nonlinear behavior. The WaveNet, which was originally a neural network architecture for audio processing, has been modified and first introduced to the analysis of mechanical signals to capture long-term dependencies in mechanical systems and generate high-quality signals. A novel nonlinear system identification method has been proposed using a modified WaveNet-based approach that constructs a relationship between the vibration response and the nonlinear elements in the inverse model without the need for a definite structural model. This approach uses dilated convolution for feature extraction and a multilayer perceptron for feature transition, with the addition of average pooling along the time dimension for adaptive processing of varying length data, which are more computationally efficient and widely applicable. The 13-layer modified WaveNet models have been designed and applied to the problem. Comparisons with other baseline models were made to demonstrate the method’s superiority in terms of accuracy, effectiveness, and robustness. Additionally, the method has been applied to predict composite models of friction and elastic curves, demonstrating its ability to handle diverse and complex problems.