JNTUK B.Tech AI Techniques (Elective – IV) for R13 Batch.

JNTUK B.Tech AI Techniques (Elective – IV) R13 Syllabus for Engineering it gives you detail information of AI Techniques (Elective – IV) syllabus.

Preamble

The aim of this course is to study the AI techniques such as neural networks and fuzzy systems. The course focuses on the application of AI techniques to electrical engineering.

Learning Objectives

• To study various methods of AI
• To study the models and architecture of artificial neural networks.
• To study the ANN paradigms.
• To study the fuzzy sets and operations.
• To study the fuzzy logic systems.
• To study the applications of AI.

UNIT–I

Introduction to AI techniques : Introduction to artificial intelligence systems– Humans and Computers – Knowledge representation – Learning process – Learning tasks – Methods of AI techniques.

UNIT–II

• Neural Networks : Organization of the Brain – Biological Neuron – Biological and Artificial neuron Models, MC Culloch-pitts neuron model, Activation functions, Learning rules, neural network architectures- Single-layer feed-forward
• networks: – Perceptron, Learning algorithm for perceptron- limitations of Perceptron model

UNIT–III

ANN paradigm : Multi-layer feed-forward network (based on Back propagation algorithm)– Radial-basisn function networks- Recurrent networks (Hopfield networks).

UNIT – IV

Classical and Fuzzy Sets : Introduction to classical sets – properties – Operations and relations – Fuzzy sets –Membership – Uncertainty – Operations – Properties – Fuzzy relations – Cardinalities – Membership functions.

UNIT–V

Fuzzy Logic System Components : Fuzzification – Membership value assignmen – Development of rule base and decision making system – Defuzzification to crisp sets – Defuzzification methods – Basic hybrid system.

UNIT–VI

Application of AI techniques : Load forecasting – Load flow studies – Economic load dispatch – Load frequency control – Reactive power control – Speed control of dc and ac motors.

Text Books

• Neural Networks, Fuzzy logic, Genetic algorithms: synthesis and applications by S.Rajasekaran and G.A. Vijayalakshmi Pai – PHI Publication.
• Fuzzy logic with fuzzy applications- by T.J. Ross, TMH.

Reference Books

• Introduction to Artificial Neural Systems – Jacek M. Zurada, Jaico Publishing House, 1997.
• Fundamentals of Neural Networks Architectures, Algorithms and Applications – by laurene Fausett, Pearson.
• Neural Networks, Algorithms, Applications and programming Techniques by James A. Freeman, David M. Skapura.
• Introduction to Neural Networks using MATLAB 6.0 by S N Sivanandam, S Sumathi, S N Deepa TMGH

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