Deep Learning-Based System Identification: An Overview

System identification is a crucial task in various fields like control systems, signal processing, and machine learning. It involves modeling and predicting the behavior of complex systems using mathematical equations or algorithms. However, developing accurate models can be challenging when dealing with nonlinear systems or large datasets. Recently, deep learning (DL) has been applied to system identification tasks with promising results. DL is a subset of machine learning that utilizes neural networks to learn patterns in data.
In this article, we explore the integration of DL into traditional system identification methods. We begin by explaining the fundamental concepts of system identification and DL. The authors discuss the advantages of combining these two techniques, such as improved accuracy and efficiency. They also highlight some challenges associated with implementing DL for system identification, including selecting appropriate neural network architectures and dealing with overfitting issues.
The article continues by delving into various DL approaches for system identification, including feedforward networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). The authors provide detailed explanations of each architecture, along with examples of their applications in system identification. They also discuss the importance of properly selecting hyperparameters to optimize model performance.
The article concludes by emphasizing the potential benefits of integrating DL into system identification methods. The authors argue that this integration can lead to more accurate predictions and improved control over complex systems. However, they also acknowledge the need for further research in this area to fully understand the capabilities and limitations of DL-based system identification techniques.
In summary, this article aims to bridge the gap between traditional system identification methods and deep learning techniques. By providing a comprehensive overview of the current state of the field, it provides valuable insights into the potential applications and challenges of integrating DL into system identification. The article’s concise yet informative style makes it accessible to readers with various levels of expertise in these fields.