MR4204 Intelligence in Systems Syllabus:
MR4204 Intelligence in Systems Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
1. To understand the basic concepts of artificial intelligence available in systems.
2. To learn and understand the basic concepts of artificial neural networks
3. To impart knowledge genetic algorithm.
4. To understand the components and concepts in fuzzy systems
5. To demonstrate the various applications of Artificial intelligence in systems
UNIT I INTRODUCTION
Approaches to intelligent control. Architecture for intelligent control. Symbolic reasoning system, rulebased systems, the AI approach. Knowledge representation. Expert systems
UNIT II ARTIFICIAL NEURAL NETWORKS
Concept of Artificial Neural Networks and its basic mathematical model, McCulloch-Pitts neuron model, simple perceptron, Adaline and Madeline, Feed-forward Multilayer Perceptron. Learning and Training the neural network. Hopfield network, Self-organizing network and Recurrent network. Neural Network based controller – Case studies: Identification and control of linear and nonlinear dynamic systems using Matlab-Neural Network toolbox. Stability analysis of Neural-Network interconnection systems.
UNIT III GENETIC ALGORITHM
Basic concept of Genetic algorithm and detail algorithmic steps, adjustment of free parameters. Solution of typical control problems using genetic algorithm. Concept on some other search techniques like tabu search and ant-colony search techniques for solving optimization problems.
UNIT IV FUZZY LOGIC SYSTEM
Introduction to crisp sets and fuzzy sets, basic fuzzy set operation and approximate reasoning. Introduction to fuzzy logic modeling and control. Fuzzification, inferencing and defuzzification. Fuzzy knowledge and rule bases. Fuzzy modeling and control schemes for nonlinear systems. Self-organizing fuzzy logic control. Fuzzy logic control for nonlinear time delay system – Applications: Implementation of fuzzy logic controller using Matlab fuzzy-logic toolbox. Stability analysis of fuzzy control systems.
UNIT V ADVANCED LEARNING
Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
CO1. Understand the various intelligence concepts available in the mechatronics system.
CO2. Demonstrate and design any mechatronics system with artificial neural networks
CO3. Select and implement appropriate techniques and genetic algorithm
CO4. Design and implement the real time application with fuzzy logic.
CO5. Familiar with advanced learning techniques
REFERENCES:
1. Padhy.N.P.(2005), Artificial Intelligence and Intelligent System, Oxford University Press.
2. KOSKO.B. “Neural Networks and Fuzzy Systems”, Prentice-Hall of India Pvt. Ltd., 1994.
3. Jacek.M.Zurada, “Introduction to Artificial Neural Systems”, Jaico Publishing House,1999.
4. KLIR G.J. & FOLGER T.A. “Fuzzy sets, uncertainty and Information”, Prentice-Hall of India Pvt. Ltd., 1993.
5. Zimmerman H.J. “Fuzzy set theory-and its Applications”-Kluwer Academic Publishers, 1994.
6. Driankov, Hellendroon, “Introduction to Fuzzy Control”, Narosa Publishers.
7. Goldberg D.E. (1989) Genetic algorithms in Search, Optimization and Machine learning, Addison Wesley.