ABSTRACT
System identification is the process of determining a dynamic model for an unknown system that can subsequently be used for feedback control purposes. On the other hand, state estimation involves determining the unknown internal states of a dynamic system. System identification provides one technique for estimating the states. The area of system identification has received significant attention over the past decades and now it is a fairly mature field with many powerful methods available at the disposal of control engineers. Online system identification methods to date are based on recursive methods such as least squares, for most systems that are expressed as linear in the parameters (LIP). To overcome this LIP assumption, neural networks (NNs) are now employed for system identification since these networks learn complex mappings from a set of examples. As seen in the previous chapters, due to NN approximation properties (Cybenko 1989) as well as the inherent adaptation features of these networks, NN present a potentially appealing alternative to modeling of nonlinear systems. Moreover, from a practical perspective, the massive parallelism and fast adaptability of NN implementations provide additional incentives for further investigation.
