Approximate nonlinear system linearization with neural networks

Researches have shown that neural networks have the ability to approximate a function as well as its derivatives. In this paper a novel method of approximate nonlinear system linearization with neural networks is proposed. The network approximator is designed to integrate the involutive equation of a nonlinear system whether the integrability condition is satisfied or not. This result offers a promising opportunity to introduce neural network theory into nonlinear system control.