Course Objectives: To understand the concepts of Artificial Intelligence and Expert Systems, to enable to design Intelligent Controls

Overview of Intelligent control techniques and Expert Systems: Intelligent control techniques, Concept of artificial intelligence, General Concepts of Expert System

Artificial Neural Networks: Artificial Neuron models, Types of activation functions, Neural network architectures, Neural Learning: Correlation, Competitive, Feedback based weight adaptation, Evaluation of networks, Quality of results, Generalizability, Computational resources, Supervised learning: Perceptrons, linear separability, Multilayer networks, Backpropagation algorithm and its varianta, Unsupervised learning, Winner-take all networks, Adaptive resonance theory, Self organizing maps, Hopfield networks, Boltzman machines, Support Vector Machine, Typical application in identification, Optimization, Pattern recognition. Applications of ANN inProcess control, Robotics and other industrial control methods.

Fuzzy Logic: Fuzziness vs probability, Crisp logic vs fuzzy logic, Fuzzy sets and systems, Operations on sets, Fuzzy relations, Membership functions, Fuzzy rule generation, De fuzzy controllers, Type-2 Fuzzy Logic Controllers, Multi-layer and other advanced Fuzzy Logic Models, Applications of Fuzzy Logic. Applications inProcess control, Robotics and other industrial control methods.

Evolutionary Computation: Introduction to optimization problem, constraints, objective functions, unimodel/ multimodel problems, classical techniques/evolutionary computational techniques Genetic Algorithms and its Operators, variants of Genetic Algorithm and its use in Engineering Process Control

Laboratory Work:Experiments around Input and output using Fuzzy logic, Graphical analysis of various control systems using Fuzzy logic, Dynamical and optimal training for neural networks, Algorithms around GA.

Course Outcomes: After the completion of this course the student will be able to:

• acquire knowledge about Artificial Intelligence and Expert Systems

• ability to understand Artificial Neural Networks

• understand Fuzzy Logic

• understand Evolutionary Computation

• acquire knowledge about Hybrid Techniques

• understand applications of Intelligent techniques in Process control, Robotics and industrial control systems

Recommended Books

1. Narayana, Y., Artificial Neural Networks, Prentice-Hall of India (1999).

2. Rich, E., and Knight, K., Artificial intelligence, McGraw Hill (1991) 2^nd ed.

3. Ross, J. T., Fuzzy Logic with Engineering Applications, John Wiley (2004) 2^nd ed.