Neural Network Based System Identification Toolbox 20 PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Department of Automation, Building 326, 2800 Lyngby, Denmark, Technical University of Denmark

The toolbox contains a number of m-files for training and evaluation of multi-layer perceptron type neural networks. There are functions for working ordinary feed-forward networks as well as for identification of nonlinear dynamic systems and time-series analysis. Not all functions have been thoroughly tested yet and even if I haven*t been able to find any bugs, I can*t guarantee that you won*t encounter any problems.

Below an overview of the files contained in this directory is given along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help in the MATLAB command window. Along with the m- and c-files in this directory you will find a manual for the toolbox. Start by printing this out and read the release notes. A number of simple demonstration programs have been made to illustrate how most of the functions work. Run these to get an idea of what the toolbox provides. Be careful with the demos, though. Due to the existence of several local minimas, the results will often vary a great deal from run to run.

The present toolbox: *Neural Network Based System Identification Toolbox*, contains a large number of functions for training and evaluation of multilayer perceptron type neural networks. The main focus is on the use of neural networks as a generic model structure for the identification of nonlinear dynamic systems. The System Identification Toolbox provided by The MathWorks, Inc., has thus been a major source of inspiration in constructing this toolbox, but the functions work completely independent of the System Identification Toolbox as well as of the Neural Network Toolbox (also provoded by the MathWorks, Inc.).

Although the use in system identification will be emphasized below, the tools made available here can also be used for time-series analysis or simply for training and evaluation of ordinary feed-forward networks (for example for curve fitting). This chapter starts by giving a brief introduction to multilayer perceptron networks and how they may be trained. The rest of the tutorial will then address the nonlinear system identification problem, and a number of functions supporting this application are described. A small demonstration example, giving an idea of how the toolbox is used in practice concludes the chapter.

A reference guide which details the use of the different functions is given in Chapter 2. It should be emphasized that this is not a text book on how to use neural networks for system identification. A good understanding of system identification (see for example Ljung, 1987) and of neural networks (see Hertz et al., 1991 or Haykin, 1993), are important requirements for understanding this tutorial. Naturally, the textbook of N?rgaard et al. (2000) is recommended literature as it specifically covers the use of neural networks in system identification.

The manual could have been written in more textbook like fashion, but it is the author*s conviction that it is better to leave elementary issues out to motivate the reader to obtain the necessary insight into identification and neural network theory before using the toolbox. Understanding is the best way to avoid that working with neural networks becomes a *fiddlers paradise*!

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