Investigation of Training Algorithms for Neural Networks in Nonlinear Dynamic Systems

Abstract

This paper explores various training algorithms for neural networks applied to the modeling and control of nonlinear dynamic systems. Nonlinear systems are characterized by complexities that linear models cannot capture, making traditional methods insufficient. Neural networks, due to their universal approximation capabilities, have emerged as powerful tools for learning complex system behaviors. The study compares several supervised and unsupervised training algorithms, including backpropagation, Levenberg–Marquardt, resilient backpropagation, and gradient descent with momentum. Simulation experiments demonstrate the effectiveness and limitations of each method in dynamic adaptation, convergence speed, and generalization ability. The findings support the selection of appropriate algorithms depending on the system's structure and real-time demands.