Chapter 7
FUZZY-NEURAL AND
NEURAL-FUZZY
CONTROL
So far we have discussed two distinct methods for building controllers: fuzzy
and neural. Often the choice of method is dictated by the data available on
the plant involved. If the data are pairs of numbers, we may turn to a neural
method, and if the data are rules, fuzzy methods may be appropriate. Neural
methods provide learning capability, whereas fuzzy methods provideflexible
knowledge-representational capability. Integrating these two methodologies, in
control in particular and in intelligent technologies in general, can lead to better
technologies that take advantage of the strengths of each methodology and at
the same time overcome some of the limitations of the individual techniques.
In this chapter, we discuss methods for combining neural and fuzzy methods
to build controllers. There are many ways in which these methods can be com-
bined. Complicated controllers can have different component problems, each
of which may require different types of processing, but such complex situations
are beyond the scope of this book. Within a single component, there are still
basically two ways that fuzzy and neural technologies can be combined. In
one direction, fuzzy logic can be introduced into neural networks to enhance
knowledge representation capability of conventional neural networks. This can
be done by introducing fuzzy concepts within neural networks ñ that is, at the
levels of inputs, weights, aggregation operations, activation functions, and out-
puts. Standard mathematical models for neurons can, for example, be changed
to ìfuzzy neuronsî with t-norms and t-conorms used to build aggregation oper-
ations. This leads to afuzzy-neural systemwith which one can present fuzzy
inputs and develop an analog of the conventional backpropagation algorithm for
training.
In the other direction, neural networks can be used in fuzzy modeling and
control to provide fuzzy systems with learning capabilities. These methods lead