soft computing and hard computing
Definition of Soft Computing
Soft computing was developed in the year 1981 by “Lotfi A. Zadeh”. Soft computing includes Fuzzy Logic, Neuro-Computing, Evolutionary and Genetic Computing and Probabilistic Computing. It is the composition of methodologies designed to model and enable solution to real world problems. Soft Computing aims to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve close resemblance with human decisions.
Goals achieved by Soft Computing:-
- It is a new multidisciplinary field, to construct new generation of artificial intelligence.
- It is used to develop intelligent machines to provide solutions to real world world problems.
Soft Computing vs Hard Computing:-
1. Soft Computing is tolerant of imprecision, uncertainty, partial truth and approximation whereas Hard Computing requires a precisely state analytic model.
2. Soft Computing is based on fuzzy logic, neural sets, and probabilistic reasoning whereas Hard Computing is based on binary logic, crisp system, numerical analysis and crisp software.
3. Soft computing has the characteristics of approximation and dispositionality whereas Hard computing has the characteristics of precision and categoricity.
4. Soft computing can evolve its own programs whereas Hard computing requires programs to be written.
5. Soft computing can use multivalued or fuzzy logic whereas Hard computing uses two-valued logic.
6. Soft computing incorporates stochasticity whereas Hard computing is deterministic.
7. Soft computing can deal with ambiguous and noisy data whereas Hard computing requires exact input data.
8. Soft computing allows parallel computations whereas Hard computing is strictly sequential.
9. Soft computing can yield approximate answers whereas Hard computing produces precise answers.