Nonlinear Systems Identification Using Deep Dynamic Neural Networks

Abstract

Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with complex behavior. Three deep neural network structures are trained on sequential data, and we investigate the effectiveness of these networks in modeling associated characteristics of the underlying dynamical systems. We carry out similar evaluations on select publicly available system identification datasets. We demonstrate that deep neural networks are effective model estimators from input-output data

American Control Conference, 2017

References in corpus (1)

• Recurrent Neural Network Regularization

Cited by in corpus (9)

• Deep and Confident Prediction for Time Series at Uber
• Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
• Perspective: magnetic nanoparticles in theranostic applications
• Few-shot Learning for Time-series Forecasting
• A Neural Network Ensemble Approach to System Identification
• A Survey on Machine Learning Applied to Dynamic Physical Systems
• Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules
• Learning Region of Attraction for Nonlinear Systems
• Assured Neural Network Architectures for Control and Identification of Nonlinear Systems