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B.E Artificial Intelligence and Machine Learning Neural Networks and Fuzzy Systems Syllabus - Mumbai University 2026

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Neural-Networks-and-Fuzzy-Systems.pdf
Semester 7 · Fourth Year AI-ML

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Syllabus for Neural Networks and Fuzzy Systems

Semester 7 · Fourth Year AI-ML

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. 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. Membership Functions: Features of Membership Functions, Fuzzification, Methods of membership value assignments. Fuzzy Rules, Reasoning, and Inference System 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. 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. Fuzzy Inference System (FIS): Mamdani FIS, Sugeno FIS, Comparison between Mamdani and Sugeno FIS. Associative Memory Networks Introduction: Basics of associative memory networks, Training algorithms for Pattern Association. Types of Networks: Radial basis function network : architecture training algorithm, Auto associative Memory Network – Architecture, Flowchart of training process, Training algorithm, Testing algorithm, Hetero- associative Memory Network- Architecture and Testing algorithm, Bidirectional Associative Memory(BAM) Network- Architecture, Discrete BAM, Continuous BAM. Unsupervised Learning Networks Introduction Fixed weight competitive nets, Maxnet, Maxican net, Hamming Network Kohonen Self- Organizing Feature Maps: Basic concepts, Architecture, Flowchart, Algorithms, Kohonen Self-Organizing Motor map Training algorithm. 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. Special Network 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 Hybrid Computing Neuro-Fuzzy Hybrid Systems: Introduction to Neuro-Fuzzy systems, Comparison of Fuzzy systems 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.

Textbooks

  • 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
  • 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 References: 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 Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

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.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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