What is Soft Computing?
Basically, soft computing may be viewed in two related perspectives. In one view, soft computing–in contrast to hard computing–is aimed at an accommodation with the pervasive imprecision of the real world, exploiting the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. In another view, soft computing is a coalition or consortium of methodologies that share this objective. At present, the principal members of the coalition are: fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). What is important about these methodologies is that, for the most part, they are complementary rather than competitive. A central tenet of soft compuring is that, in general, better performance can be achieved by using the constituent methodologies in combination rather than in a stand-alone mode. A prominent example i
Traditional electronic computations tend to be black and white. When working in binary code, with sequences of zeros and ones, there is no chance for anything else but simple “yes or no” answers. While that may be an adequate way of computing for many tasks, soft computing takes a different approach. In short, soft computing allows the computer to take on a certain level of imprecision in its work. Some may equate this with artificial intelligence, in that it is similar to the way the human brain works. From a human perspective, soft computing introduces compromises into a computer’s processing, that are not present in hard computing. There are times, when the answer to a question may be yes or no, but there is not yet enough information to calculate definitively what the answer is. Traditional computers facing this situation will simply stop and wait until there is enough information to draw a precise conclusion. Soft computing is, in essence, the capability of a computer to provide a
