Perceptron Networks
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When it is time-consuming or expensive tomodel a plant using the basic laws of physics,
a neural network approach can be an alternative. From a control engineer’s viewpoint a twolayer
perceptron network is sufficient. It is indicated how to model a dynamic plant using
a perceptron network.

A neural network is basically a model structure and an algorithm for fitting the model to
some given data. The QHWZRUN DSSURDFK to modelling a plant uses a generic nonlinearity
and allows all the parameters to be adjusted. In this way it can deal with a wide range
of nonlinearities. /HDUQLQJ is the procedure of training a neural network to represent the
dynamics of a plant, for instance in accordance with Fig. 1. The neural network is placed
in parallel with the plant and the error h between the output of the system and the network
outputs, the SUHGLFWLRQ HUURU, is used as the training signal. Neural networks have a potential
for intelligent control systems because they can learn and adapt, they can approximate
nonlinear functions, they are suited for parallel and distributed processing, and they naturally
model multivariable systems. If a physical model is unavailable or too expensive to
develop, a neural network model might be an alternative.
Networks are also used for FODVVLILFDWLRQ. An example of an industrial application concerns
acoustic quality control (Meier, Weber & Zimmermann, 1994). A factory produces
ceramic tiles and an experienced operator is able to tell a bad tile from a good one by hitting
it with a hammer; if there are cracks inside, it makes an unusual sound. In the quality control
system the tiles are hit automatically and the sound is recorded with a microphone. A
neural network (a so-called Kohonen network) tells the bad tiles from the good ones, with
an acceptable rate of success, after being presented with a number of examples.
The objective here is to present the subject from a FRQWURO engineer’s viewpoint. Thus,
there are two immediate application areas:
 Models of (industrial) processes, and
 controllers for (industrial) processes.
In the early 1940sMcCulloch and Pitts studied the connection of several basic elements
based on a model of a neuron, and Hebb studied the adaptation in neural systems. Rosenblatt
devised in the late 50s the 3HUFHSWURQ now widely used. Then in 1969 Minsky and
Papert pointed to several limitations of the perceptron, and as a consequence the research in
the field slowed down for lack of funding. The catalyst for today’s level of research was a
series of results and algorithms published in 1986 by Rumelhart and his co-workers. In the
90s neural networks and fuzzy logic came together in neurofuzzy systems since both techniques
are applied where there is uncertainty. There are now many real-world applications
ranging from finance to aerospace.
There are many neural network architectures such as the perceptron, multilayer perceptrons,
networks with feedback loops, self-organising systems, and dynamical networks,
together with several different learning methods such as error-correction learning, competitive
learning, supervised and unsupervised learning (see the textbook by Haykin, 1994).
Neural network types and learning methods have been organised into a brief classification
scheme, a WD[RQRP\ (Lippmann, 1987).
Neural networks have already been examined from a control engineer’s viewpoint (Miller,
Sutton & Werbos in Hunt, Sbarbaro, Zbikowski & Gawthrop, 1992). Neural networks can
be used for system identification (forward modelling, inverse modelling) and for control,
such as supervised control, direct inverse control, model reference control, internal model
control, and predictive control (see the overview article by Hunt et al., 1992). Within the
realm of modelling, identification, and control of nonlinear systems there are applications
to pattern recognition, information processing, design, planning, and diagnosis (see the
overview article by Fukuda&Shibata, 1992). Hybrid systems using neural networks, fuzzy
sets, and artificial intelligence technologies exist, and these are surveyed also in that article.
A systematic investigation of neural networks in control confirms that neural networks can
be trained to control dynamic, nonlinear, multivariable, and noisy processes (see the PhD
thesis by Sørensen, 1994). Somewhat related is Nørgaards investigation (1996) of their
application to system identification, and he also proposes improvements to specific control
designs. A comprehensive systematic classification of the control schemes proposed in
the literature has been attempted by Agarwal (1997). His taxonomy is a tree with ’control
schemes using neural networks’ at the top node, broken down into two classes: ’neural
network only as aid’ and ’neural network as controller’. These are then further refined.
There are many commercial tools for building and using neural networks, either alone
or together with fuzzy logic tools; for an overview, see the database CITE (MIT, 1995).
Neural network computations are naturally expressed in matrix notation, and there are several
toolboxes in the matrix language Matlab, for example the commercial neural network
toolbox (Demuth& Beale, 1992), and a university developed toolbox for identification and
control, downloadable from the World Wide Web (Nørgaard, NNSYSID with NNCTRL ).
In the wider perspective of VXSHUYLVRU\ control, there are other application areas, such
as robotic vision, planning, diagnosis, quality control, and data analysis (data mining). The
strategy in this lecture note is to aim at all these application areas, but only present the
necessary and sufficient neural network material for understanding the basics.

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