EE

BTEEE604C: Artificial Neural Network Syllabus for EE 6th Sem 2019-20 DBATU (Elective-VI)

Artificial Neural Network detailed syllabus scheme for Electrical Engineering (EE), 2019-20 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 6th Sem Scheme of Electrical Engineering (EE), 2019-20 Onwards, do visit EE 6th Sem Scheme, 2019-20 Onwards. For the Elective-VI scheme of 6th Sem 2019-20 onwards, refer to EE 6th Sem Elective-VI Scheme 2019-20 Onwards. The detail syllabus for artificial neural network is as follows.

Artificial Neural Network Syllabus for Electrical Engineering (EE) 3rd Year 6th Sem 2019-20 DBATU

Artificial neural network

Course Outcomes:

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.
Get it on Google Play.

UNIT 1

Introduction and ANN Structure : Biological neurons and artificial neurons. Model of an ANN. Activation functions used in ANNs. Typical classes of network architectures. Mathematical Foundations and Learning mechanisms : Re-visiting vector and matrix algebra. State-space concepts. Concepts of optimization. Error-correction learning. Memory-based learning. Hebbian learning. Competitive learning. 8

UNIT 2

Single layer perceptrons : Structure and learning of perceptrons. Pattern classifier -introduction and Bayes’ classifiers. Perceptron as a pattern classifier. Perceptron convergence. Limitations of a perceptrons. 6

UNIT 3

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.
Get it on Google Play.

UNIT 4

Radial Basis Function Networks : Pattern separability and interpolation. Regularization Theory.Regularization and RBF networks.RBF network design and training. Approximation properties of RBF 6

UNIT 5

Competitive Learning and Self organizing ANN : General clustering procedures. Learning Vector Quantization (LVQ). Competitive learning algorithms and architectures. Self organizing feature maps. Properties of feature maps. 6

UNIT 6

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.
Get it on Google Play.

Reference Books:

  1. NPTEL course

For detail syllabus of all subjects of Electrical Engineering (EE) 6th Sem 2019-20 onwards, visit EE 6th Sem Subjects of 2019-20 Onwards.

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