AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

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HYBRID INTELLIGENT SYSTEMS
Step 6:
Step 7:
Step 9:
Place the created offspring chromosomes in the new population.
Repeat Step 3 until the size of the new population becomes equal to the
size of the initial population, and then replace the initial (parent)
population with the new (offspring) population.
Go to Step 2, and repeat the process until a specified number of
generations (typically several hundreds) is considered.
The above algorithm can dramatically reduce the number of fuzzy IF-THEN
rules needed for correct classification. In fact, several computer simulations
(Ishibuchi et al., 1995) demonstrate that the number of rules can be cut down to
less than 2 per cent of the initially generated set of rules. Such a reduction leaves
a fuzzy classification system with relatively few significant rules, which can then
be carefully examined by human experts. This allows us to use fuzzy evolutionary
systems as a knowledge acquisition tool for discovering new knowledge
in complex databases.
8.7 Summary
In this chapter, we considered hybrid intelligent systems as a combination of
different intelligent technologies. First we introduced a new breed of expert
systems, called neural expert systems, which combine neural networks and rulebased
expert systems. Then we considered a neuro-fuzzy system that was
functionally equivalent to the Mamdani fuzzy inference model, and an adaptive
neuro-fuzzy inference system, ANFIS, equivalent to the Sugeno fuzzy inference
model. Finally, we discussed evolutionary neural networks and fuzzy evolutionary
systems.
The most important lessons learned in this chapter are:
. Hybrid intelligent systems are systems that combine at least two intelligent
technologies; for example, a combination of a neural network and a fuzzy
system results in a hybrid neuro-fuzzy system.
. Probabilistic reasoning, fuzzy set theory, neural networks and evolutionary
computation form the core of soft computing, an emerging approach to
building hybrid intelligent systems capable of reasoning and learning in
uncertain and imprecise environments.
. Both expert systems and neural networks attempt to emulate human intelligence,
but use different means. While expert systems rely on IF-THEN rules
and logical inference, neural networks use parallel data processing. An expert
system cannot learn, but can explain its reasoning, while a neural network
can learn, but acts as a black-box. These qualities make them good candidates
for building a hybrid intelligent system, called a neural or connectionist
expert system.
. Neural expert systems use a trained neural network in place of the knowledge
base. Unlike conventional rule-based expert systems, neural expert systems