Soft Computing, also known as fuzzy logic, is a field of computer science which deals with the properties of “soft” or “fuzzy” behavior of systems, and with the techniques used to design them. It is a branch of artificial intelligence which attempts to apply the principles of natural computing, such as the rules of logic, to system design.
Soft Computing is based on fuzzy logic, a variant of classical logic which deals with the behavior of systems when some of the variables are unknown, imprecise, or vague. It is used to model problems which are based on fuzzy sets, and has the ability to determine a reasonable solution when there are many possible solutions or outcomes. This type of system is often applied in the fields of SCADA systems, industrial automation, and robotics. It is also used to perform predetermined tasks with flexibility.
The methods employed in Soft Computing include neural networks, genetic algorithms, fuzzy logic, probability theory, and various types of decision making. These techniques are used to give machines the ability to learn, adapt and self-organize in order to make difficult decisions.
Soft Computing is closely related to the fields of biology and cognitive science, and is often used to design intelligent agents, expert systems, and autonomous robots, among other tasks. This type of computing is suitable for dealing with problems which involve uncertainty, imprecision, and approximations. It is a rapidly evolving field which continues to be researched and used in various engineering applications.