In-depth explanation of "soft computing"

Abstract

In contrast to classical computing, soft computing works with approximations of models and provides answers to difficult, real-world situations. Soft computing, in contrast to traditional computing, can tolerate fuzziness, doubt, half-truths, and approximations. The human brain serves as the inspiration for soft computing. Fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems are all examples of the kinds of soft computing that form the basis of the field. A significant field of study in automated control engineering, soft computing has been around since the 1980s but has only recently been widely accepted as a viable theory and set of methods in its own right. These days, soft computing methods are effectively used in a wide variety of consumer, business, and manufacturing settings. It is certain that the methodologies and application areas of soft computing will continue to develop with the emergence of low-cost and extremely high performance digital processors and the drop in the cost of memory chips. This article provides an overview of contemporary soft computing approaches, contrasting them with more conventional hard computing methods and detailing the benefits and drawbacks of each.