Soft computing is a problem solving technology. It tends to fuse synergically different aspects of fuzzy logic, neural networks, evolutionary algorithms, and non-linear distributed systems in order to define and implement hybrid systems. Some of the hybrid systems are neuro-fuzzy, fuzzy-genetic and fuzzy cellular neural networks. Soft computing results in innovative solutions in the sectors of intelligent control, classification, and modeling and simulating complex non-linear dynamic systems.
Combining new computation techniques allow systems to achieve a higher tolerance level towards imprecision and approximation. Therefore the new software and hardware products are robust. Fuzzy Logic plays a key as it is mainly concerned with imprecision and approximate reasoning. Neural Networks is used for learning.
Soft computing is used for approximate models to give solution to complex problems. This is in contrast with hard computing which deals with procise models providing accurate solutions.
Prof Lotfi Zadeh introduced the term, soft computing. The objective was to emulate human mind as closely as possible. The word, soft means flexible, adjustable, random, vague, approximate, imprecise, perceivable, porous and non-deterministic.
Advantages
Applications
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