Some Topics in Neural Network-based System Identification

Embargo until
2027-08-01
Date
2023-07-20
Journal Title
Journal ISSN
Volume Title
Publisher
Johns Hopkins University
Abstract
Neural network-based system identification is a modeling technique that uses a neural network to learn the relationship between the input states and output states of a system. The neural network is trained on a set of input-output pairs, and once trained, it can be used to predict the system response to new inputs. Neural network-based system identification is particularly useful for modeling complex systems with nonlinear dynamics and unknown or time-varying behavior. It is commonly used in control engineering, signal processing, and robotics to model and control complex systems.
Description
Keywords
System Identification, Neural Networks, Machine Learning, Data-driven Modelling
Citation