Soft Computing
Soft Computing differs from
conventional (hard) computing in that, unlike hard computing, it is tolerant of
imprecision, uncertainty, partial truth, and approximation. In effect, the role
model for soft computing is the human mind. Principal
constituents of Soft Computing are Neural Networks, Fuzzy
Logic, Evolutionary Computation, Swarm Intelligence and Bayesian Networks. The
successful applications of soft computing suggest that the impact of soft
computing will be felt increasingly in coming years. Soft computing is
likely to play an important role in science and engineering, but eventually its
influence may extend much farther.
Soft Computing became a formal Computer Science area of study in the early 1990's.Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.
You can download Soft Computing seminar abstract from here.
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