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.
• 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...