Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.

1. Soft Computing
By: R.Geethika ramani
13JT1A0524

2. Contents:
 Introduction
 History of Soft Computing
 Components of Soft Computing
 Soft Computing vs hard computing
 Applications
 Example
 Merits
 Demerits
 Conclusion

3. Introduction:
 Soft computing (SC) solutions are unpredictable, uncertain and
between 0 and 1.
 Human brain is the basic concept of Soft Computing.

4. History of Soft Computing:
 First proposed by Lotfi Zadeh in 1973.
 Later development made in soft computing.

5. Components of Soft Computing:
 Fuzzy Logic
 Probabilistic Reasoning
 Evolutionary Computation
 Machine Learning
 Neural Networks

6. Fuzzy Logic:
 Not only formal boolean values, but also range between
them
0.60

7. Probabilistic Reasoning:
 Probabilistic reasoning is using logic and probability to
handle uncertain situations.
Prob:"X"
O/P:1
O/P:2
O/P:n

8. Evolutionary Computation:
 Choosing an exact solution from variety of solutions.
 Technically they belong to the family of trial and error
problem solvers.
Prob:"X" O/P:2

9. Machine Learning:
 This is a process of learning a system by itself without
any algorithm.

10. Neural Networks:
Working of Biological Neuron: Working of Artificial Neuron:

11. Connection types of Neurons:
• Static Connection: • Dynamic Connection:

12. Topologies of Neurons:
• Single-level Topology: • Multi-level Topology: • Recurrent:

13. Learning Methods of Neurons:
 Supervised learning
 Unsupervised learning
 Reinforcement

14. Soft computing Vs Hard Computing:
 A small "FAILURE"
 Time
 Cost
 Imprecision values

15. Applications:
 Robotics
 Google translate
 Artificial intelligence
 Sensor based technologies
 Latest gaming technology

16. Example:

17. Merits:
 Work by learning.
 Work will be automatically shared.
 No need to write any algorithms

18. Demerits:
 Needs to understand before working with neural
networks.
 Takes large time for connecting neurons

19. Conclusion:
On behalf-of writing algorithm for each and every specific task, Soft
computing is very flexible and more powerful way to perform our
task in easy way.