Introduction to Soft Computing

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What is Soft Computing ? (adapted from L.A. Zadeh) Lecture 1 What is soft computing Techniques used in 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.



What is Hard Computing?
• Hard computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time. • Many analytical models are valid for ideal cases. • Real world problems exist in a non-ideal environment. 3

What is Soft Computing? (Continued)
• The principal constituents, i.e., tools, techniques, of Soft Computing (SC) are – Fuzzy Logic (FL), Neural Networks (NN), Support Vector Machines (SVM), Evolutionary Computation (EC), and – Machine Learning (ML) and Probabilistic Reasoning (PR)


Premises of Soft Computing
• The real world problems are pervasively imprecise and uncertain • Precision and certainty carry a cost

Guiding Principles of Soft Computing
• The guiding principle of soft computing is:
– Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.



Hard Computing
• Premises and guiding principles of Hard Computing are
– Precision, Certainty, and rigor.

Implications of Soft Computing
• Soft computing employs NN, SVM, FL etc, in a complementary rather than a competitive way. • One example of a particularly effective combination is what has come to be known as "neurofuzzy systems.” • Such systems are becoming increasingly visible as consumer products ranging from air conditioners and washing machines to photocopiers, camcorders and many industrial applications. 7 8

Many contemporary problems do not lend themselves to precise solutions such as – – Recognition problems (handwriting, speech, objects, images Mobile...
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