Soft Computing

Soft Computing (SC)

• Scheme & Syllabus UOM R2016 [ Syllabus ]

• R16 M Tech CS - Scheme [ PDF ]

  • R16 M Tech CS - Scheme [ PDF ]

  • R16 M Tech CS2 - Advanced Soft Computing [ PDF ]

Course Materials [ PPTs/PDFs/Handouts ]

• Ch00 [02] Prerequisite [ PDF ]

• Ch01 [06] Fuzzy Set Theory  [ PDF ]

  • Classical Set Theory

• Ch02 [06] Fuzzy Rules, Reasoning, and Inference System

• Ch03 [09] Neural Network-I [ PDF ] [ PDF ]

  • Hebb Net Example Mar 2021 [ PDF ]

• Ch04 [10] Neural Network-II [ PDF ]

• Ch05 [10] Genetic Algorithms [ PDF ]

• Ch06 [09] Hybrid Computing

Learning Soft Computing [ PDFs ]

• Unit I

  • Introduction [ PDF ]

• Unit II

  • Artificial Neural Network [ PDF ]

    • Hebb Net Example Mar 2021 [ PDF ]

  • Supervised Learning Network [ PDF ]

  • Associative Memory Networks [ PDF ]

• Unit III

  • Unsupervised Learning Networks [ PDF ]

  • Special Networks [ PDF ]

  • Third Generation Neural Networks [ PDF ]

• Unit IV

  • Introduction to Fuzzy Logic, Classical Sets and Fuzzy sets [ PDF ]

  • Classical Relations and Fuzzy Relations [ PDF ]

  • Genetic Algorithm [ PDF ]

  • Optimization [ PDF ]

Module Wise Question Bank

• Advanced Soft Computing Chapter wise Questions - Updated on May 2022 [ For Syllabus ]

  • Theory Questions [ Google Spreadsheet] 

  • MCQs [ Google Spreadsheet] 

• SC Question Bank - Updated on July 2021

  • Theory Questions [ Google Spreadsheet ] 

OLD Question Papers

• • BE-IT_SEM8_SC_DEC17 [ PDF ] 

  • BE-IT_SEM8_SC_DEC18 [ PDF ]

  • BE-IT_SEM8_SC_MAY16 [ PDF ]

  • BE-IT_SEM8_SC_MAY17 [ PDF ]

  • BE-IT_SEM8_SC_MAY18 [ PDF ]

  • BE-IT_SEM8_SC_MAY19 [ PDF ]

Lab Session

• • Google Colab

    • Image classification Using Deep Learning: Flower classification [Lab123_Flower_classification.ipynb] 

      • Lab 1: Initial configuration steps of Google colab and importing Tensorflow and other libraries

      • Lab 2: Download and explore the dataset and Visualize the data

      • Lab 3: Create the model and visualized training results and predict on new data.

    • Fuzzy Inference System [ Lab45_fis.ipynb ]

  • Virtual Lab

    • Soft Computing Tools in Engineering Lab [click here] 

Books 

1. S. N. Sivanandan and S.N. Deepa, Principles of Soft Computing, Wiley India, 2007, ISBN: 10: 81-265-1075-7.

2. J.S.R. Jang, C. T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, PHI Learning Private Limited-2014

3. Neural Networks: A Classroom Approach, Satish Kumar, Tata McGraw-Hill Education, 2004/2007

4. Simon Haykin, Neural Networks A Comprehensive Foundation, Second Edition, Pearson Education-2004

5. David E. Goldberg, Genetic Algorithms, in search, optimization and Machine Learning, Pearson 

References

1. Anupam Shukla, Ritu Tiwari, Rahul Kala, Real Life Applications of Soft Computing, CRC Press, Taylor & Francis Group, 2010.

2. Genetic Algorithms and Genetic Programming Modern Concepts and Practical Applications © 2009 Michael Affenzeller, Stephan Winkler, Stefan Wagner, and Andreas Beham, CRC Press

3. Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms And Applications, Pearson