Hard computing

Soft computing

Soft computing refers to a collection of computational techniques in computer science, machine learning and some engineering disciplines, which study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Soft Computing uses soft techniques contrasting it with classical artificial intelligence hard computing techniques. Hard computing is bound by a Computer Science concept called NP-Complete, which means, in layman's terms, that there is a direct connection between the size of a problem and the amount of resources needed to solve the problem (there are problems so large that it would take the lifetime of the Universe to solve them, even at super computing speeds). Soft computing aids to surmount NP-complete problems by using inexact methods to give useful but inexact answers to intractable problems.

Introduction

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.

Components of soft computing include:

Neural networks (NN)
Fuzzy systems (FS)
Evolutionary computation (EC), including:

Swarm intelligence
Ideas about probability including:

Bayesian network

Chaos theory

Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing techniques are intended to complement each other.

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.

References

Bibliography

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Abraham,A., Rule Based Expert Systems, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 909-919, 2005.

Abraham,A., Evolutionary Computation, Handbook for Measurement Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 920-931, 2005.

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Abraham,A., Das, S., and Roy, S., Swarm Intelligence Algorithms for Data Clustering, Soft Computing for Knowledge Discovery and Data Mining, Oded Maimon and Lior Rokach (Eds.), Springer Verlag, Germany, ISBN 978-0-387-69934-9, pp. 279-313, 2007.