UNIQUE SERIES AKTU Syllabus
BEE056

Neural Networks & Fuzzy System

L-T-P3-0-0 Credits3 Session2024-25 TypeTheory
Appears in 2 semesters · 2 branches

B.Tech Electrical Engineering · Sem 5 (Departmental Elective-II) · B.Tech Electrical & Electronics Engineering · Sem 5 (Departmental Elective-II)

Download PDF

Syllabus

5 units
Unit1

Unit 1: Neural Networks-1(Introduction & Architecture)

Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions, Neural network architecture: single layer and multilayer feed forward networks, recurrent networks. Various learning techniques; perception and convergence rule, Auto-associative and hetro- associative memory.

Unit2

Unit 2: Neural Networks-II (Back propogation networks)

Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting back propagation training, applications.

Unit3

Unit 3: Fuzzy Logic-I (Introduction)

Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp conversion.

Unit4

Unit 4: Fuzzy Logic –II (Fuzzy Membership, Rules)

Membership functions, interference in fuzzy logic, fuzzy if-then rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications & Defuzzificataions, Fuzzy Controller, Industrial applications.

Unit5

Unit 5: Fuzzy Neural Networks

L-R Type fuzzy numbers, fuzzy neutron, fuzzy back propogation (BP), architecture, learning in fuzzy BP, inference by fuzzy BP, applications.

Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Apply the concepts of feed forward neural networks and their learning techniques.K3
  2. CO2Comprehend the architecture, develop algorithms and apply the concepts of back propagation networks.K5
  3. CO3Differentiate between the fuzzy and the crisp sets, apply the concepts of fuzziness and the fuzzy set theory.K4
  4. CO4Select the membership functions, write rules and develop the fuzzy controller for Industrial applications.K5
  5. CO5Demonstrate the working of fuzzy neural networks and identify its applications. KL- Bloom’s Knowledge Level (K1, K2, K3, K4, K5, K6) K1 – Remember K2 – Understand K3 – Apply K4 – Analyze K5 – Evaluate K6 – CreateK3

Text Books

  1. Kumar Satish, “Neural Networks” Tata Mc Graw Hill
  2. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications” Prentice Hall of India.

Reference Books

  1. Siman Haykin, “Neural Netowrks” Prentice Hall of India
  2. Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India

Official AKTU syllabus

This subject is on page 38 of the original AKTU PDF.

Official AKTU syllabus PDF ↗