ABSTRACT
The primary considerations of traditional hard computing are precision, certainty, and rigor. In contrast, the principal notion in soft computing is that precision and certainty carry a cost; and that computation, reasoning, and decision making should exploit (wherever possible) the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for obtaining low-cost solutions. Soft computing is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. There is no universally best soft computing method; choosing particular soft computing tool(s) or some combination with traditional methods is entirely dependent on the particular application, and it requires human interaction to decide on the suitability of a blended approach. The area of soft computing involves three main aspects: fuzzy systems, which are ideally suited for problem representations and user interactions; neural networks for making models; and evolutionary programming for finding a solution or making an inference. The chapter ends with an introduction to rough sets that discover knowledge in the form of business rules from imprecise and uncertain data sources.
