Identification and control of dynamical systems using neural networks.

Area of Science:

• Control Engineering
• Artificial Intelligence
• Dynamical Systems Theory

Background:

• Nonlinear dynamical systems present significant challenges in accurate modeling and control.
• Traditional control methods often struggle with the complexity and unpredictability of these systems.
• Neural networks offer a powerful alternative for addressing these complexities.

Purpose of the Study:

• To demonstrate the efficacy of neural networks for identifying and controlling nonlinear dynamical systems.
• To explore novel configurations of interconnected multilayer and recurrent neural networks.
• To present a unified approach for analyzing these complex network architectures.

Main Methods:

• Utilizing static and dynamic backpropagation for neural network parameter adjustment.
• Developing interconnected multilayer and recurrent network models.
• Employing simulation to validate the proposed identification and control schemes.

Main Results:

• Neural networks proved effective for both identification and control tasks in nonlinear systems.
• Simulation results confirmed the practical feasibility of the suggested identification and adaptive control strategies.
• Novel network configurations demonstrated enhanced performance.

Conclusions:

• Neural networks provide a viable and effective tool for the identification and control of nonlinear dynamical systems.
• The proposed unified approach to interconnected networks is beneficial for theoretical and practical advancements.
• Further research into theoretical aspects is warranted for broader application.