Neuro-fuzzy Approach - Computational Intelligence in Time Series Forecasting - page 217

6
Neuro-fuzzy Approach
6.1 Motivation for Technology Merging
Contemporary intelligent technologies have various characteristic features that can
be used to implement systems that mimic the behaviour of human beings. For
example, expert systems are capable of reasoning about the facts and situations
using the rules out of a specific domain, etc . The outstanding feature of neural
networks is their capability of learning, which can help in building artificial
systems for pattern recognition, classification, etc . Fuzzy logic systems, again, are
capable of interpreting the imprecise data that can be helpful in making possible
decisions. On the other hand, genetic algorithms provide implementation of
random, parallel solution search procedures within a large search space. Therefore,
in fact, the complementary features of individual categories of intelligent
technologies make them ideal for isolated use in solving some specific problems,
but not well suited for solving other kinds of intelligent problem. For example, the
black-box modelling approach through neural networks is evidently well suited for
process modelling or for intelligent control, but less suitable for decision making.
On the other hand, the fuzzy logic systems can easily handle imprecise data, and
explain their decisions in the context of the available facts in linguistic form;
however, they cannot automatically acquire the linguistic rules to make those
decisions. Such capabilities and restrictions of individual intelligent technologies
have actually been a central driving force behind their fusion for creation of hybrid
intelligent systems capable of solving many complex problems.
The permanent growing interest in intelligent technology merging, particularly
in merging of neural and fuzzy technology, the two technologies that complement
each other (Bezdek, 1993), to create neuro-fuzzy or fuzzy-neural structures, has
largely extended the capabilities of both technologies in hybrid intelligent systems.
The advantages of neural networks in learning and adaptation and those of fuzzy
logic systems in dealing with the issues of human-like reasoning on a linguistic
level, transparency and interpretability of the generated model, and handling of
uncertain or imprecise data, enable building of higher level intelligent systems. The