Page 23 - Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

12 INTRODUCTION

The subtitle of the book, ‘An Engineering Approach using MATLAB’, indi-
cates that its focus is not just on the formal description of classification,
parameter estimation and state estimation methods. It also aims to
provide practical implementations of the given algorithms. These imple-
mentations are given in MATLAB.MATLAB is a commercial software
package for matrix manipulation. Over the past decade it has become
the de facto standard for development and research in data-processing
applications. MATLAB combines an easy-to-learn user interface with a
simple, yet powerful language syntax, and a wealth of functions orga-
nized in toolboxes. We use MATLAB as a vehicle for experimentation,
the purpose of which is to find out which method is the most appro-
priate for a given task. The final construction of the instrument can also
be implemented by means of MATLAB, but this is not strictly necessary.
In the end, when it comes to realization, the engineer may decide to
transform his design of the functional structure from MATLAB to other
platforms using, for instance, dedicated hardware, software in
embedded systems or virtual instrumentation such as LabView.
For classification we will make use of PRTools (described in Appendix E),
a pattern recognition toolbox for MATLAB freely available for non-com-
mercialuse.MATLAB itself has many standard functions that are useful for
parameter estimation and state estimation problems. These functions are
scattered over a number of toolboxes. Appendix F gives a short overview of
these toolboxes. The toolboxes are accompanied with a clear and crisp
documentation, and for details of the functions we refer to that.
Each chapter is followed by a few exercises on the theory provided.
However, we believe that only working with the actual algorithms will
provide the reader with the necessary insight to fully understand the
matter. Therefore, a large number of small code examples are provided
throughout the text. Furthermore, a number of data sets to experiment
with are made available through the accompanying website.

1.4 REFERENCES

Brignell, J. and White, N., Intelligent Sensor Systems, Revised edition, IOP Publishing,
London, UK, 1996.
Finkelstein, L. and Finkelstein A.C.W., Design Principles for Instrument Systems in
Measurement and Instrumentation (eds. L. Finkelstein and K.T.V. Grattan), Pergamon
Press, Oxford, UK, 1994.
Regtien, P.P.L., van der Heijden, F., Korsten, M.J. and Olthuis, W., Measurement Science
for Engineers, Kogan Page Science, London, UK, 2004.