Dear Mahdieh,

The applications of soft computing approach have proved two main advantages:(1) it made solving nonlinear problems, in which mathematical models are not available, possible and (2) it introduced the human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.

In summary, the advantages of employing soft computing is its capability to tolerate

imprecision, uncertainty, and partial truth to achieve tractability and robustness on simulating human decision-making behavior with low cost. In other words, soft computing provides an opportunity to represent ambiguity in human thinking with the uncertainty in real life

For more on this topic, please read the article entitled "ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS " by Santosh Kumar Das et al. Rupak Bhattacharyya et al. (Eds) : ACER 2013, pp. 59–68, 2013. © CS & IT-CSCP 2013

http://airccj.org/CSCP/vol3/csit3206.pdf

and  SOFT COMPUTING TECHNIQUES (Chapter-3) Zedah L. A. (1992)

http://shodhganga.inflibnet.ac.in/bitstream/10603/11748/9/09_chapter_03.pdf

Hoping this will be helpful,

Rafik

Dear Mahdieh,

Prof. Karaman provided a very good answer.

There are many types of Soft Computing models but the famous ones include Neural Networks, Fuzzy Logic and Genetic Algorithm. Each has its own applications.

Soft computing methodologies, such as neural networks, fuzzy logic, and genetic algorithms,  have shown the following  advantages in various appilcations 

• We do not need to specify the model structure a priori
• Soft computing methods are nonlinear and can approximate complex nonlinear and dynamic systems

Source: Melin, P., Castillo, O., & Ramírez, E. G. (2007). Analysis and Design of Intelligent Systems Using Soft Computing Techniques (Vol. 41). Springer Science & Business Media.

The guiding principle of soft computing is exploit the tolerance for imprecision, uncertainties and approximation to achieve  tractability and robustness at a low Computational  cost.

Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation.

In contrast to hard computing, soft computing is a collection of methods (fuzzy sets, rough sets, neutral nets, etc.) for dealing with ambiguous situations like imprecision and uncertainty, for example, human expressions like “high profit at reasonable risks”.

The objective of applying soft computing is to obtain robust solutions at reasonable costs.

Sources:

Bansod, N. A., Kulkarni, M., & Patil, S. H. (2005). Soft Computing-A Fuzzy Logic Approach. Soft Computing (Allied Publishers 2005), 73.

http://www.igi-global.com/dictionary/soft-computing/27620

The advantages could be;

1. Tolerance to imprecision.

2. To be able to model problems which have element of uncertainty of non-statistical kind.

3. To be able to capture uncertainty and vagueness.

4. To be able to construct or percieve "Linguistic Variables".

5. To be able to provide approximate reasoning.

6. To be able to form rules and equations of the form:

 If temperature is more

 Then allow small quantity of coolant

Color = red.

If Tomato is RED

Then it is RIPE