Soft Computing

Soft Computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. Principal constituents of Soft Computing are Neural Networks, Fuzzy Logic, Evolutionary Computation, Swarm Intelligence and Bayesian Networks. The successful applications of soft computing suggest that the impact of soft computing will be felt increasingly in coming years. Soft computing is likely to play an important role in science and engineering, but eventually its influence may extend much farther
Soft Computing became a formal Computer Science area of study in the early 1990’s.Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional
mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost
Unlike hard computing schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that inductive reasoning plays a larger role in soft computing than in hard computing. Components of soft computing include: Neural Network, Perceptron, Fuzzy Systems, Baysian Network, Swarm Intelligence and Evolutionary Computation.
The highly parallel processing and layered neuronal morphology with learning abilities of the human cognitive faculty ~the brain~ provides us with a new tool for designing a cognitive machine that can learn and recognize complicated patterns like human faces and Japanese characters. The theory of fuzzy logic, the basis for soft computing, provides mathematical power for the emulation of the higher-order cognitive functions ~the thought and perception processes. A marriage between these evolving disciplines, such as neural computing, genetic algorithms and fuzzy logic, may provide a new class of computing systems ~neural-fuzzy systems ~ for the emulation of higher-order cognitive power.