To expose the students to the concepts of feed forward neural networks.
To provide adequate knowledge about feedback neural networks
To provide adequate knowledge about fuzzy and neuro-fuuzy systems
To provide comprehensive knowledge of fuzzy logic control to real time systems.
To provide adequate knowledge of genetic algorithms and its application to economic dispatch and unit commitment problems.
UNIT I ARCHITECTURES – ANN 9
Introduction – Biological neuron – Artificial neuron – Neuron model – Supervised and unsupervised learning- Single layer – Multi layer feed forward network – Learning algorithm- Back propagation
UNIT II NEURAL NETWORKS FOR CONTROL 9
Feedback networks – Discrete time Hopfield networks – Transient response of continuous time system – Applications of artificial neural network – Process identification – Neuro controller for inverted pendulum.
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
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.
TOTAL: 45 PERIODS
Ability to understand and apply basic science, circuit theory, Electro-magnetic field theory
control theory and apply them to electrical engineering problems.
To understand and apply computing platform and software for engineering problems.
- Laurance Fausett, Englewood Cliffs, N.J., ‘Fundamentals of Neural Networks’, Pearson Education, 1992.
- Timothy J. Ross, ‘Fuzzy Logic with Engineering Applications’, Tata McGraw Hill, 3rd Edition , 2010..
- S.N.Sivanandam and S.N.Deepa, Principles of Soft computing, Wiley India Edition, 2nd Edition,2013
- Simon Haykin, ‘Neural Networks’, Pearson Education, 2003.
- John Yen & Reza Langari, ‘Fuzzy Logic – Intelligence Control & Information’, Pearson Education, New Delhi, 2003.
- M.Gen and R,Cheng, Genetic algorithms and optimization, Wiley Series in Engineering Design and Automation, 2000.
- Hagan, Demuth, Beale, “ Neural Network Design”, Cengage Learning, 2012.
- N.P.Padhy, “ Artificial Intelligence and Intelligent Systems”, Oxford, 2013.
- William S.Levine, “Control System Advanced Methods,” The Control Handbook CRC Press 2011.