Syllabus AI TECHNIQUES Question papers, Answers, important Question AI TECHNIQUES R13 Regulation B.Tech JNTUK-kakinada Syllabus download

Syllabus ,AI TECHNIQUES Question papers, Answers, important Question AI TECHNIQUES, R13 Regulation, B.Tech , JNTUK,Syllabus, download,

AI TECHNIQUES Syllabus R13 Regulation RT42024C

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

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

Reference Books

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

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