Expert System detailed syllabus scheme for Instrumentation Engineering (IS), 2019 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.
For 8th Sem Scheme of Instrumentation Engineering (IS), 2019 Pattern, do visit IS 8th Sem Scheme, 2019 Pattern. For the Department Level Optional Course-4 scheme of 8th Sem 2019 regulation, refer to IS 8th Sem Department Level Optional Course-4 Scheme 2019 Pattern. The detail syllabus for expert system is as follows.
Expert System Syllabus for Instrumentation Engineering BE 8th Sem 2019 Pattern Mumbai University
Course Objectives:
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Course Outcomes:
The students will able to
- Identify various networks and learning algorithms in artificial neural network (ANN).
- Define Fuzzy set, rules and membership function and also defuzzification for a given problem. 3.Identify areas of application for Expert Systems. 4Apply the concepts of ANN and Fuzzy Logic in solving engineering problems and implementing controllers. 5.Discuss various concepts of Genetic Algorithm 6.Identify various hybrid control strategies.
Prerequisites:
Knowledge of control systems, optimization technique, expert system, Neural network and Genetic algorithm.
Module 1
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Module 2
Introduction to Fuzzy Logic Fuzzy set theory – Fuzzy sets – Operation on Fuzzy sets – Scalar cardinality, fuzzy cardinality, union and intersection, complement, equilibrium points, aggregation, projection, composition, decomposition, cylindrical extension, fuzzy relation – Fuzzy membership functions, De- fuzzification. 09 CO2
Module 3
Introduction to Expert System What are Expert Systems, Features of Expert System, Basic activities of expert system and the areas in which they solve problems, Prospector systems-features, working. Knowledge representation in expert systems- using rules semantic nets, frames, Types of tools available for expert system building, Stages in the development of expert system tools. Building an Expert system. 09 CO3
Module 4
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Module 5
Genetic Algorithm Basic concept of Genetic algorithm – flow chart of GA – Genetic representations – encoding – Initialization and selection, Genetic operators- Mutation, Generational Cycle, applications – Concepts on search techniques – Tabu search, Ant-colony search and Particle Swarm Optimization (PSO). 09 CO5
Module 6
Hybrid Control Schemes Neuro fuzzy systems -Adaptive neuro fuzzy inference system (ANFIS) – Optimization of membership function and rule base using Genetic Algorithm and PSO – Case study – Introduction to Support Vector Regression – Familiarization of ANFIS Tool Box. 06 CO6
Internal Assessment:
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Theory Examination:
- Question paper will comprise of 6 questions, each carrying 20 Marks.
- Total 4 questions need to be solved.
- Question No. 1 will be compulsory and based on entire syllabus wherein sub questions of 4 to 5 marks will be asked.
- Remaining questions will be mixed in nature.
- In question paper weight age of each module will be proportional to number of respective Lecture hours as mentioned in the syllabus.
Text Books:
- Stamatios V. Kartalopolous, .Understanding Neural Network and Fuzzy Logic., PHI Pvt Ltd.
- Kishan Mehrotra, .Elements of ANN., 2nd Editon, Penram International Publishing(I) Pvt.Ltd.
- Donald A. Waterman, A Guide to Expert Systems, Addison-Wesley Publishing Company
- David Goldberg. V Genetic Algorithms in Search, Optimization, and Machine Learning, Pearson Education, 2009
Reference Books:
- Laurene. V, Fausett, Fundamentals of Neural Networks, Architecture, Algorithms, and Applications, Pearson Education, 2008.
- Timothy. J, Ross, Fuzzy Logic with Engineering Applications, Wiley, Third Edition, 2010.
- Zimmermann. H.J, “Fuzzy set theory-and its Applications”- Springer international edition, 2011.
- Miller W.T, Sutton . R.S and Webrose . P.J, Neural Networks for Control, MIT Press, 1996.
- Kevin Night and Elaine Rich, Nair B., Artificial Intelligence (SIE), Mc Graw Hill-2008.
- Dan W. Patterson, Introduction to AI and ES, Pearson Education,
- Peter Jackson, Introduction to Expert Systems, 3rd Edition, Pearson Education, 2007.
- Stuart Russel and Peter Norvig AI – A Modern Approach, 2nd Edition, Pearson Education 2007
- Deepak Khemani Artificial Intelligence, Tata Mc Graw Hill Education 2013.
- Laurance Fausett, Englewood Cliffs, N.J., Fundamentals of Neural Networks, Pearson Education,1992.
- Timothy J. Ross, Fuzzy Logic with Engineering Applications, Tata McGraw Hill, 1997.
- 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.
- http://nptel.ac.in
For detail Syllabus of all subjects of Instrumentation Engineering (IS) 8th Sem 2019 regulation, visit IS 8th Sem Subjects of 2019 Pattern.