Explore the principles and applications of Nonlinear Autoregressive eXogenous model (NARX) in this comprehensive course. Learn to utilize NARX models for time series prediction and system identification with practical examples and hands-on exercises.
Welcome to our comprehensive course on NARX (Nonlinear AutoRegressive with eXogenous inputs) networks! This course is designed to provide a deep understanding of NARX networks, their significance, and practical implementation. Starting with an introduction to NARX networks, we explore their architecture, including input, hidden, and output layers, along with neuron configurations. Weight initialization techniques, hyperparameters like epochs and learning rates, and various activation and loss functions are covered extensively. You'll also learn about training methodologies such as Gradient Descent, Adam, and Stochastic Gradient Descent with Momentum. Practical sessions include data explanation, numerical examples, and implementation in MATLAB and Python, making this course essential for mastering NARX networks for real-world applications.