Abstract
Machines’ conceptual intelligence is built on the foundation of soft computing. The goal is to take use of tolerance for imprecision, ambiguity, approximate reasoning, and partial truth in order to create a close likeness to human decision-making. Soft computing is the study of reasoning, thinking, recognizing, and evaluating problems in the real world using biologically inspired methodologies. There are no mathematical models accessible in many applications, and they are impossible to use. Mathematical models don’t always provide the most accurate or precise results. Soft computing, on the other hand, establishes techniques such as neural networks, evolutionary computing, fuzzy logic, genetic algorithms, probabilistic reasoning, and statistics that are ideal for those situations. This chapter focuses on various soft computing approaches and their applications.
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