Unit I
Basic Concepts – Single Layer Perception – Multilayer Perception – Supervised and Unsupervised Learning – Back Propagation networks – Kohnen’s self-organizing networks – Hop field networks – Distance measures.
| Course Name | Soft Computing |
| Course Code | 25CSA333 |
| Program | B. Sc. in Physics, Mathematics & Computer Science (with Minor in Artificial Intelligence and Data Science) |
| Semester | Electives: Computer Science |
| Campus | Mysuru |
Basic Concepts – Single Layer Perception – Multilayer Perception – Supervised and Unsupervised Learning – Back Propagation networks – Kohnen’s self-organizing networks – Hop field networks – Distance measures.
FUZZY sets, properties, Membership functions Fuzzy operations, Applications
Classification and Regression Trees – Data Clustering Algorithms – Rule based Structure identification.
Neuro-Fuzzy Systems.
Evolutionary Computation – Survival of the Fittest – Fitness Computation – Crossover – Mutation – Reproduction – Rank space Method. Case Studies: Applications of soft computing.
Course outcome
| COs | Description |
| CO1 | Describe and identify the strategies and functions of the soft computing in smart machines |
| CO2 | Acknowledge the usefulness of a soft computing mechanism for a significant problem. |
| CO3 | Address the advantages of various neural network architectures and their limitations. |
| CO4 | Use fuzzy logic and thinking in order to address insecurity and to resolve problems of engineering and genetic algorithms to substitute issues of optimization. |
| CO5 | Find out numerous methods for solving technical and real-world problems with these models. |
TEXTBOOK/ REFERENCES:
1) Laurence Fausett, “Fundamentals of Neural Networks”, Seventh Edition, Dorling Kindersley (India)
P. Ltd 2006.
2) Satish Kumar – “Neural Networks – A Classroom Approach”, Tata McGraw-Hill, 2004.
3) Timothy J.Rose, ”Fuzzy Logic with Engineering Applications”, Third Edition, John Wiley, 2010.
4) J.S.R Jang,C.T Sun and E.Mizutani, ”Neuro-Fuzzy and Soft Computing”, Second Edition, Prentice Hall of India, 2002.
5) D.E.Goldberg ”Genetic Algorithms in search, optimization and Machine learning”, Second Edition, Addison Wesley, 2007.
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