<title>Neural control and system identification using a similarity approach</title>

Abstract

For nonlinear and adaptive control of smart structures direct and indirect neural network control strategies have been suggested. In indirect neural network control the identified plant models are usually implemented as black-box neural networks using no a priori knowledge. Designing a neural network for system identification using dimensional analysis results in neural networks, where in contrary to black-box solutions no dimensionally inhomogeneous states can occur. Furthermore, the generalization and learning properties of neural networks designed using dimensional analysis are usually improved compared to conventional black-box networks. This work describes a technique of using neural networks for system identification and control, where the neural network has been constructed according to a dimensional analysis of the governing equations.