Soft computing
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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.
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