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Home / Syllabus / B.E. (Artificial Intelligence and Data Science) / Fourth Year BE AI and DS / Semester 7 / Neural Networks and Fuzzy Systems

B.E. (Artificial Intelligence and Data Science) Neural Networks and Fuzzy Systems Syllabus - Mumbai University

This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.

Neural Networks and Fuzzy Systems.pdf
Semester 7 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Neural Networks and Fuzzy Systems

Semester 7 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 1.0 07 hours

  • 1.0 Fuzzy Set Theory
  • 1.1 Introduction to soft and hard computing Fuzzy Sets:
  • Basic definition and terminology of fuzzy sets, Classic set
  • operations; Fuzzy set operations- Union, Intersection, complement,
  • Difference; Properties of fuzzy sets.
  • 1.2 Fuzzy relations: Cartesian product of relation, Classica Relation, Cardinality of
  • fuzzy relations, Operations on Fuzzy relations, Properties of Fuzzy
  • relations, Fuzzy composition, Tolerance and Equivalence
  • Relationship.
  • 1.3 Membership Functions: Features of Membership Functions, Fuzzification, Methods of
  • membership value assignments.
  • 2.0 Fuzzy Rules, Reasoning, and Inference System
  • 2.1 Defuzzification:
  • Lambda-Cuts for Fuzzy Sets; Lambda-Cuts for Fuzzy Relations;
  • Defuzzification methods: Max-Membership Principles, Centroid
  • Method, Weighted Average Method, Mean-Max Membership, Center
  • of Sums, Center of Largest Area, First of Maxima.
  • 2.2 Fuzzy Arithmetic and Rules:
  • Fuzzy arithmetic, Fuzzy measures, Measures of Fuzziness, Truth
  • Value and Tables in Fuzzy Logic, Fuzzy Propositions, Formation of
  • rules, Decomposition of rules, Fuzzy Reasoning.
  • 2.3 Fuzzy Inference System (FIS): Mamdani FIS, Sugeno FIS, Comparison between Mamdani and
  • Sugeno FIS.
  • 3.0 Associative Memory Networks
  • 3.1 Introduction: Basics of associative memory networks, Training algorithms for Pattern Association.
  • 3.2 Types of Networks: Radial basis function network : architecture training algorithm, Auto-associative Memory Network – Architecture, Flowchart oftraining process, Training algorithm, Testing algorithm, Hetero- associative Memory Network- Architecture and Testing algorithm, Bidirectional Associative Memory(BAM) Network- Architecture,Discrete BAM, Continuous BAM.
  • 4.0 Unsupervised Learning Networks
  • 4.1 Introduction
  • Fixed weight competitive nets, Maxnet, Maxican net, Hamming
  • Network
  • 4.2 Kohonen Self- Organizing Feature Maps: Basic concepts, Architecture, Flowchart, Algorithms, Kohonen
  • Self-Organizing Motor map
  • Training algorithm.
  • 4.3 Adaptive resonance Theory: Architecture, Fundamental Operating principles, a Algorithms,
  • Adaptive Resonance Theory I – Architecture, Flowchart of Training
  • process, Training algorithm, Adaptive Resonance Theory 2 -
  • Architecture, Algorithm, Flowchart, Training algorithm, Sample
  • Values of Parameter.
  • 5.0 Special Network
  • 5.1 Introduction: Boltzmann Machine, Gaussian Machine, Probabilistic neural nets Spatio-Temporal connection network model, Ensemble neural model Extreme learning machine models, Online, Pruned, Improved Application of ELM
  • 6.0 Hybrid Computing 05
  • 6.1 Neuro-Fuzzy Hybrid Systems: Introduction to Neuro-Fuzzy systems, Comparison of Fuzzysystems and Neural networks, Characteristics of Neuro-Fuzzy systems, Classification of Neuro-Fuzzy systems. Introduction to Adaptive Neuro-Fuzzy Inference System (ANIFS), ANFS Architecture, Constraints of ANFIS, ANFIS as a Universal Approximator.

Text 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

Digital Material

  • https://onlinecourses.nptel.ac.in/noc22_ee21/preview
  • https://onlinecourses.nptel.ac.in/noc23_ge15/preview

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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