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Difference Between Soft Computing and Hard Computing
There are two types of computing methods namely, soft computing and hard computing. The basic difference between the two is that the hard computing is a conventional computing method which relies on the principles of certainty, accuracy, and inflexibility, on the other hand, the soft computing is a modern methodology that relies on the principles of approximation, flexibility, and uncertainty.
In this article, we will discuss the important differences between soft computing and hard computing. But, before going into the differences, let's start with a basic overview.
What is Soft Computing?
Soft Computing is a modern computing model that evolved to resolve non-linear problems that involve approximation, uncertainty, and imprecision. Thus, soft computing can be associated with being liberal with inexactness, uncertainty, partial truth and approximation. Soft computing mainly depends on the formal logic and probabilistic reasoning.
The term "soft computing" was first coined by Dr Lotfi Zadeh. According to Dr Zadeh, soft computing is a methodology that imitates the human brain to reason and learn in an uncertain environment. Soft computing uses multivalued logics and thus has a sophisticated nature. It is mainly used to perform parallel computations.
What is Hard Computing?
Hard Computing is a conventional approach used in computing and requires an accurately stated analytical model. The term "hard computing" too was coined by Dr Lotfi Zadeh. In fact, he coined this term before "soft computing". Hard computing depends on the binary logic and crisp system.
Hard computing uses two-valued logic. Therefore, it has a deterministic nature. It produces precise and accurate results. In hard computing, some definite control actions are defined using a mathematical model or algorithm.
The major drawback of hard computing is that it is incapable in solving the real world problems whose behavior is imprecise and their information being changing continuously. Hard computing is mainly used to perform sequential computations.
Difference between Soft Computing and Hard Computing
The following table highlights the major differences between soft computing and hard computing −
| Soft Computing | Hard Computing |
|---|---|
| It can be associated with being liberal with inexactness, uncertainty, partial truth and approximation. | It requires a precise state analytic model. |
| It depends on formal logic and probabilistic reasoning. | It depends on binary logic and crisp system. |
| It consists of approximation and dispositionality. | Its features include precision and categoricity. |
| It has a stochastic nature. | It has a deterministic nature. |
| It generally works on ambiguous and noisy data. | It works on exact data. |
| It can be used to perform parallel computations. | It is used to perform sequential computations. |
| It results in approximate results. | It produces precise results. |
| It can come out with its own programs. | The programs have to be written. |
| It incorporates randomness in its computations. | It is settled in nature. |
| It uses multivalued logic. | It uses the two-valued logic. |
Conclusion
The most significant difference that you should note here is that hard computing is a conventional approach used to solve a deterministic problem, whereas soft computing is a modern approach used to solve uncertain and imprecise problems.
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