Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time. 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. 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).
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
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
Application of Soft Computing:
Application of Soft Computing:
Ø Handwriting Recognition
Ø Image Processing and Data Compression
Ø Automotive Systems and Manufacturing
Ø Soft Computing to Architecture
Ø Decision-support Systems
Ø Soft Computing to Power Systems
Ø Neuro Fuzzy systems
Ø Fuzzy Logic Control
Ø Machine Learning Applications
Ø Speech and Vision Recognition Systems
Ø Process Control
