AKTU Neural Networks & Fuzzy Systems (BEE056) Syllabus PDF – B.Tech 3rd Year
Neural Networks & Fuzzy Systems BEE056
All syllabus, notes, question bank & PYQ for this subject
- English
- Semester 5
- 0 downloads
- 0 views
Syllabus — Neural Networks & Fuzzy Systems
Download PDFBEE056 · 3 credits · L-T-P 3-0-0
Course Outcomes
Upon the completion of the course, the student will be able to:
| CO 1 | Apply the concepts of feed forward neural networks and their learning techniques. | K3 |
| CO2 | Comprehend the architecture, develop algorithms and apply the concepts of back propagation networks. | K5 |
| CO3 | Differentiate between the fuzzy and the crisp sets, apply the concepts of fuzziness and the fuzzy set theory. | K4 |
| CO4 | Select the membership functions, write rules and develop the fuzzy controller for Industrial applications. | K5 |
| CO5 | Demonstrate the working of fuzzy neural networks and identify its applications. | K3 |
KL- Bloom’s Knowledge Level (K , K , K , K , K , K ) 1 2 3 4 5 6 K – Remember K – Understand K – Apply K – Analyze K – Evaluate K – Create 1 2 3 4 5 6
Course Content
Unit-I: 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.
Unit-II: Neural Networks-II (Back propagation networks):
Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propagation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting back propagation training, applications.
Unit-III: 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.
Unit-IV: 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.
Unit-V: Fuzzy Neural Networks:
L-R Type fuzzy numbers, fuzzy neutron, fuzzy back propogation (BP), architecture, learning in fuzzy BP, inference by fuzzy BP, applications.
Text Books
- Kumar Satish, “Neural Networks” McGraw Hill
- S.Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications” Prentice Hall of India.
Reference Books
- Siman Haykin, “Neural Netowrks” Prentice Hall of India
- Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India
Ratings & Reviews
No reviews yet — share your experience with this item.