Neural Network and Fuzzy Logic Systems EIE 8th Sem Syllabus for VTU BE 2017 Scheme

Neural Network and Fuzzy Logic Systems detail syllabus for Electronics & Instrumentation Engineering (Eie), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EI81), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other eie 8th sem syllabus for be 2017 scheme vtu you can visit EIE 8th Sem syllabus for BE 2017 Scheme VTU Subjects. The detail syllabus for neural network and fuzzy logic systems is as follows.

Module 1

Introduction. – Neural Networks, Application Scope of Neural Networks, Fuzzy Logic, Generic Algorithm, Hybrid Systems, Soft Computing. Artificial Neural Network: An Introduction. – Fundamental Concept, Evolution of Neural Networks, Basic models of Artificial Neural Networks (ANN), Important Technologies of ANNs, McCulloch-Pitts Neuron, Linear Separability.

Module 2

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

Module 3

Back -Propagation Network. – Theory, Architecture, Flowchart for training process, Training Algorithm, Learning Factors of Back-Propagation Network, Testing Algorithm of Back-Propagation Network. Radial Basis Function Network, Time Delay Neural Network, Functional Link Networks, Tree Neural Networks, wavelet neural network.

Module 4

Introduction to Fuzzy Logic, Classical sets and Fuzzy sets. Introduction to Fuzzy Logic, Classical sets (crisp sets) – Operations on Classical sets, Properties of Classical sets, Function of Mapping of Classical sets. Fuzzy sets – Fuzzy set operations, Properties of fuzzy sets. Simple Problems Classical Relations and Fuzzy Relations – Introduction, Cartesian Product of Relation, Classical Relation, Fuzzy Relation, Tolerance and Equivalence Relations, Non-interactive Fuzzy sets, Simple Problems.

Module 5

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

Course Outcomes:

After studying this course, students will be able to:

1. Compare and contrast the biological neural network and ANN.
2. Discuss the ANN for pattern classification.
3. Develop and configure ANNs with different types of functions and learning algorithms.
4. Apply ANN for real world problems.
5. Discuss the fundamentals of fuzzy logic, implementation and their functions
6. Apply fuzzy logic concepts in building automated control systems.

Question paper pattern:

• The question paper will have TEN questions.
• Each full question carry 16 marks
• There will be TWO full questions (with maximum of THREE sub questions) from each module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer FIVE full questions, selecting ONE full question from each module.

Text Books:

1. S. N. Sivanandam and S.N. Deepa, Principles of Soft Computing, 2nd Edition, Wiley India Pvt. Ltd.-2014.
2. Timothy J. Ross, Fuzzy logic with engineering applications, McGraw Hill International Edition, 1997

Reference Books:

1. Simon Haykin, Neural Networks: A comprehensive foundation, 2nd Edition, PHI, 1998.

For detail syllabus of all other subjects of BE Eie, 2017 scheme do visit Eie 8th Sem syllabus for 2017 scheme.

Dont forget to download iStudy for latest syllabus and results, class timetable and more.