Soft Computing detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 6th Sem scheme and its subjects, do visit AI&ML 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&ML Professional Elective-VII syllabus scheme. The detailed syllabus of soft computing is as follows.
Course Objectives:
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Unit 1
INTRODUCTION TO SOFT COMPUTING AND FUZZY LOGIC
Introduction – Fuzzy Logic – Fuzzy Sets, Fuzzy Membership Functions, Operations on Fuzzy Sets, Fuzzy Relations, Operations on Fuzzy Relations, Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems.
Unit II
NEURAL NETWORKS
Supervised Learning Neural Networks – Perceptrons – Backpropagation -Multilayer Perceptrons -Unsupervised Learning Neural Networks – Kohonen Self-Organizing Networks
Unit III
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Unit IV
NEURO FUZZY MODELING
ANFIS architecture – hybrid learning – ANFIS as universal approximator – Coactive Neuro fuzzy modeling – Framework – Neuron functions for adaptive networks – Neuro fuzzy spectrum -Analysis of Adaptive Learning Capability
Unit V
APPLICATIONS
Modeling a two input sine function – Printed Character Recognition – Fuzzy filtered neural networks – Plasma Spectrum Analysis – Hand written neural recognition – Soft Computing for Color Recipe Prediction.
Course Outcomes:
- Understand the fundamentals of fuzzy logic operators and inference mechanisms
- Understand neural network architecture for AI applications such as classification and clustering
- Learn the functionality of Genetic Algorithms in Optimization problems
- Use hybrid techniques involving Neural networks and Fuzzy logic
- Apply soft computing techniques in real world applications
Practical Exercises
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Text Books:
- SaJANG, J.-S. R., SUN, C.-T., & MIZUTANI, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ, Prentice Hall,1997
- Himanshu Singh, Yunis Ahmad Lone, Deep Neuro-Fuzzy Systems with Python
- With Case Studies and Applications from the Industry, Apress, 2020
Reference Books:
- roj Kaushik and Sunita Tiwari, Soft Computing-Fundamentals Techniques and Applications, 1st Edition, McGraw Hill, 2018.
- S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.
- Samir Roy, Udit Chakraborthy, Introduction to Soft Computing, Neuro Fuzzy and Genetic Algorithms, Pearson Education, 2013.
- S.N. Sivanandam, S.N. Deepa, Principles of Soft Computing, Third Edition, Wiley India Pvt Ltd, 2019.
- R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence – PC Tools”, AP Professional, Boston, 1996
For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 6th Sem, visit AI&ML 6th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.