Page 93 - REC :: B.E. EEE CURRICULUM & SYLLABUS - R2017

Dept of EEE, REC

 To provide knowledge on neural networks and learning methods for neural networks;
 To study the basics of genetic algorithms and their applications in optimization and planning;
 To understand the ideas of fuzzy sets, fuzzy logic and fuzzy inference system;
 To impart knowledge on students tools and techniques of Soft Computing;
 To provide skills on theoretical and practical aspects of Soft Computing.

UNIT I ARCHITECTURES– ANN 9
Introduction – Biological neuron – Artificial neuron – McCullock Pitt’s neuron model – Supervised and
unsupervised learning- Single layer – Multi layer feed forward network – Learning algorithm- -Back
propagation network.

UNIT II NEURAL NETWORKS FOR CONTROL 9
Feedback networks – Discrete time Hopfield networks –Kohonen self-organising feature maps–
Applications of artificial neural network - Process identification – Neuro controller for inverted pendulum
– Optical neural network.

UNIT III FUZZY SYSTEMS 9
Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification – Defuzzification – Fuzzy rules -
Membership function – Knowledge base – Decision-making logic – Introduction to neuro fuzzy system-
Adaptive fuzzy system.

UNIT IV APPLICATION OF FUZZY LOGIC SYSTEMS 9
Fuzzy logic control: Home heating system - liquid level control - aircraft landing- inverted pendulum –
fuzzy PID control, Fuzzy based motor control.

UNIT V GENETIC ALGORITHMS 9
Introduction-Biological background – Traditional Optimization Techniques - Gradient and Non-gradient
search – GA operators – Representation – Selection – Cross Over – Mutation - constraint handling
methods – applications to economic dispatch and unit commitment problems.
TOTAL: 45 PERIODS
OUTCOMES:
On the completion of the course, the students will be able to
 realize basics of soft computing techniques and also their use in some real life situations.
 analyse the problems using neural networks techniques.
 obtain the solution using different fuzzy logic techniques
 determine the genetic algorithms for different modelling.
 evaluate the various soft computing techniques.

TEXT BOOKS:
1. LauranceFausett, Englewood cliffs, N.J., “Fundamentals of Neural Networks”, Pearson
Education, 1994.
2. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, Tata McGraw Hill, Third
edition, 2010.

Curriculum and Syllabus | B.E. Electrical and Electronics Engineering | R2017 Page 93