EC 49 C: Neural Network and Fuzzy Logic Syllabus for ET 8th Sem 2017 DBATU (Elective-X)

Neural Network and Fuzzy Logic detailed syllabus scheme for Electronics & Telecommunication Engineering (ET), 2017 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.

For 8th Sem Scheme of Electronics & Telecommunication Engineering (ET), 2017 Onwards, do visit ET 8th Sem Scheme, 2017 Onwards. For the Elective-X scheme of 8th Sem 2017 onwards, refer to ET 8th Sem Elective-X Scheme 2017 Onwards. The detail syllabus for neural network and fuzzy logic is as follows.

Neural Network and Fuzzy Logic Syllabus for Electronics & Telecommunication Engineering (ET) 4th Year 8th Sem 2017 DBATU

Course Objectives:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
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Course Outcomes:

1. The student will be able to obtain the fundamentals and types of neural networks.
2. The student will have a broad knowledge in developing the different algorithms for neural networks.
3. Student will be able analyze neural controllers.
4. Student will have a broad knowledge in Fuzzy logic principles.
5. Student will be able to determine different methods of Deffuzification

Unit 1

Introduction
Biological neurons, McCulloch and Pitts models of neuron, Types of activation function, Network architectures, Knowledge representation, Learning process: Error-correction learning, Supervised learning, Unsupervised learning, Learning Rules

Unit 2

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
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Unit 3

Multilayer Perception
Derivation of the back-propagation algorithm, Learning Factors.

Unit 4

Radial Basis and Recurrent Neural Networks
RBF network structure theorem and the reparability of patterns, RBF learning strategies, K-means and LMS algorithms, comparison of RBF and MLP networks, Hopfield networks: energy function, spurious states, error performance.

Unit 5

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Unit 6

Fuzzy logic
Fuzzy sets, Properties, Operations on fuzzy sets, Fuzzy relation Operations on fuzzy relations, The extension principle, Fuzzy mean Membership functions, Fuzzification and defuzzification methods, Fuzzy controllers.

Reference/Text Book:

1. Simon Haykin, “Neural Network a – Comprehensive Foundation”, Pearson Education.
2. Dr. S. N. Sivanandam, Mrs S.N. Deepa Introduction to Soft computing tool Wiley Publication.
3. Satish Kumar Neural Networks: A classroom Approach Tata McGraw-Hill.
4. Zurada J.M., “Introduction to Artificial Neural Systems, Jaico publishers.
5. Thimothv J. Ross, “Fuzz V Logic with Engineering Applications”, McGraw.
6. Ahmad Ibrahim, “Introduction to Applied Fuzzy Electronics’, PHI.
7. Rajsekaran S, VijaylakshmiPai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, PHI.
8. Hagan, Demuth, Beale, eNeural Network Design!, Thomson Learning
9. Christopher M Bishop Neural Networks for Pattern Recognition, Oxford Publication.
10. William W Hsieh Machine Learning Methods in the Environmental Sciences Neural Network and Kernels Cambridge Publication.
11. Dr. S. N. Sivanandam, Dr. S. Sumathi Introduction to Neural Network Using Matlab Tata McGraw-Hill

For detail syllabus of all subjects of Electronics & Telecommunication Engineering (ET) 8th Sem 2017 onwards, visit ET 8th Sem Subjects of 2017 Onwards.