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What is 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. 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. The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them Zadeh's 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 computing and genetic computing in soft computing came at a later point.

At this juncture, the principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC) Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks, chaos theory and parts of learning theory. What is important to note is that soft computing is not a melange. 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 constituent methodologies in SC are complementary rather than competitive. Furthermore, soft computing may be viewed as a foundation component for the emerging field of conceptual intelligence.

• Fuzzy Systems
• Neural Networks
• Evolutionary Computation
• Machine Learning
• Probabilistic Reasoning

Importance of Soft Computing

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• The complementarity of FL, NC, GC, and PR has an important consequence: in many cases a problem can be solved most effectively by using FL, NC, GC 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.
  
  The conceptual structure of soft computing suggests that students should be trained not just in fuzzy logic, neurocomputing, genetic programming, or probabilistic reasoning but in all of the associated methodologies, though not necessarily to the same degree.
  
  At present, the BISC Group (Berkeley Initiative on Soft Computing) comprises close to 600 students, professors, employees of private and non-private organizations and, more generally, individuals who have interest or are active in soft computing or related areas. Currently, BISC has over 50 Institutional Affiliates, with their ranks continuing to grow in number.
  
  At Berkeley, BISC provides a supportive environment for visitors, postdocs and students who are interested in soft computing and its applications. In the main, support for BISC comes from member companies.

???? Write difference between Soft Computing & Hard Computing.
"Soft computing" is just automating process of computation. "Hard computing" means you are just doing computation process according to your needs only. For eg. there are kind of problems[identifying vowels and counting them in any given sentence] which a human brain can compute very easily. This is an example of "Soft Computing". Our human brain scans vowels and counts them in seconds. Now coming to "Hard computing" - you make a program for this same above scenario and run it. That is part of "Hard Computing". You are telling the computer -just a box to compute(or process) according to your needs. So that kind of computing is a part of "Hard computing". "Soft computing" is much much times faster than "Hard Computing". Basic constituents of "Soft Computing" include Fuzzy Logic , Neural computing, Evolutionary computation, Machine learning and probabilistic reasong etc.

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