Computer Science

A glimpse into Soft computing

Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them my 1965 paper on fuzzy sets; the 1973 paper on the analysis of complex systems and decision processes; and the 1979 report (1981 paper) on possibility theory and soft data analysis. The inclusion of neural network theory in soft computing came at a later point.

At this juncture, the principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming belief networks, genetic algorithms, chaos theory and parts of learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is a partnership in which each of the partners contributes a distinct methodology for addressing problems in its domain. In this perspective, the principal contributions of FL, NN and PR are complementary rather than competitive.

Implications of soft computing
The complementarity of FL, NN and PR has an important consequence: in many cases a problem can be solved most effectively by using FL, NN and PR in combination rather than exclusively. A striking 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 and camcorders. Less visible but perhaps even more important are neurofuzzy systems in industrial applications. What is particularly significant is that in both consumer products and industrial systems, the employment of soft computing techniques leads to systems which have high MIQ (Machine Intelligence Quotient). In large measure, it is the high MIQ of SC-based systems that accounts for the rapid growth in the number and variety of applications of soft computing - and especially fuzzy logic. The conceptual structure of soft computing suggests that students should be trained not just in neural network theory or fuzzy logic or probabilistic reasoning but in all of the associated methodologies, though not necessarily to the same degree.

The successful applications of soft computing and the rapid growth of it suggest that the impact of soft computing will be felt increasingly in coming years. Soft computing is likely to play an especially important role in science and engineering, but eventually its influence may extend much farther. In many ways, soft computing represents a significant paradigm shift in the aims of computing - a shift which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity. In this perspective, what is important on soft computing is that it provides a platform for the advancement of soft computing - a platform which lowers barriers between the constituents of soft computing and facilitates international cooperation on a global scale.

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Computational Intelligence Group at Vrije University - Amsterdam]
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