This document discusses soft computing techniques. Soft computing deals with inexact solutions and uses techniques like fuzzy systems, neural networks, machine learning, and probabilistic reasoning. It summarizes the key differences between hard and soft computing, providing examples of each technique. The document concludes by listing some common applications of soft computing such as handwriting recognition, image processing, and decision support systems.

1. Soft Computing

2. Group Members
Nabarun Paul
Nilanjan Banerjee
Niraj Khaitan
Nirban Kumar

3. What is Soft Computing
• Soft computing is a term applied to a field
within computer science which is
characterized by the use of inexact solutions
for problems which has no known method to
compute an exact solution.
• It uses uncertainty, partial truth, and
approximation as input.

4. Hard Computing v/s Soft Computing
Hard Computing
Deals with precise value.
Accurate output is needed.
Useful in critical systems.
Soft Computing
Deals with assumptions.
Accuracy is not necessary.
Useful for routine, control, decision making tools.

5. Techniques in Soft Computing
• Fuzzy Systems
• Neutral Networks
• Machine Learning
• Probabilistic Reasoning

6. Fuzzy Systems
• Fuzzy system is a superset of conventional
logic that has been extended to handle the
concept of partial truth- truth values between
"completely true" and "completely false".
• Based on fuzzy set theory and fuzzy logic.
• Most applications in control and decision
making.

7. Example of Fuzzy system

8. Neutral Networks
• Neural Network is an information processing
paradigm that is inspired by the way biological
nervous systems, such as the brain, process
information.
• Essentially a function approximator.
Transforms inputs into outputs to the best
of its ability

9. Example of Neutral Networks

10. Machine Learning
• Pattern recognition based on training data,
classification supervised by instructor.
• Unsupervised machine learning is also used
where the machine learns from the given data
by detecting patterns.

11. Example of Machine Learning
?
Instructor
Orange
Apple

12. Probabilistic Reasoning
• Dealing with incomplete and uncertain data is
an important part of many artificial
intelligence systems
• Approaches
– Classical Probability Theory
– Fuzzy Set Theory

13. Advantages of Soft Computing
• Models based on human reasoning.
• Closer to human thinking and biologically
inspired
• Models can be
Linguistic
Comprehensible
Fast when computing
Effective in practice.

14. APPLICATIONS OF SOFT COMPUTING
• Handwriting Recognition
• Image Processing and Data Compression
• Automotive Systems and Manufacturing
• Soft Computing to Architecture
• Decision-support Systems

15. THANK YOU