Computational Intelligence: An Introduction - Computational Intelligence in Time Series Forecasting - page 2

1
Computational Intelligence: An Introduction
1.1 Introduction
Within the artificial intelligence society the term computational intelligence is
largely understood as a collection of intelligent computational methodologies, such
as fuzzy-logic-based computing, neurocomputing, and evolutionary computing,
that help in solving complex computational problems in science and technology,
not solvable or at least not easily solvable by using the conventional mathematical
methods.
1.2 Soft Computing
The research activity in the area of combined application of intelligent computing
technologies was initiated by Zadeh (1994), who has coined the term soft
computing, which he defined as a “collection of methodologies that aim to exploit
the tolerance for imprecision and uncertainty to achieve tractability, robustness,
and low solution cost”. According to Zadeh, the principal constituents of soft
computing are fuzzy logic, neurocomputing, and probabilistic reasoning.
The reason for the need of soft computing was, in Zadeh's opinion, that we live
in a pervasively imprecise and uncertain world and that precision and certainty
carry a cost. Therefore, soft computing should be seen as a partnership of distinct
methods, rather than as a homogeneous body of concepts and techniques.
Initially, as the main partnership members of soft computing, also called its
principal constituents, the following technologies have been seen:
x fuzzy logic , which has to deal with the imprecisions in computing and to
perform the approximate reasoning
x neurocomputing, which is required for learning and recognition purposes
x probabilistic reasoning , which is needed for dealing with the uncertainty
and belief propagation phenomena