SRM University. Faculty of Engineering and Technology. Department of Electronics and Communication Engineering

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1 SRM University Faculty of Engineering and Technology Department of Electronics and Communication Engineering Course code: EC0054 Course title: Neural network and fuzzy Semester: VII Course time: Jan 2011-March 2011 Staff Time table: Day Hour Timing Mon 5 th th th Fri 6 th Location: SRM Tech Park Faculty Details: Name of the Faculty Office Visiting Hours Mail ID Mr. B Srinath Tech Park : am to 4.00 pm Bsrinath86@gmail.com Reference books: 1. Freeman J.A. and Skapura B.M., "Neural Networks, Algorithms Applications and Programming Techniques", Addison-Wesely, George J Klir and Tina A Folger, "Fuzzy sets, uncertainty and information", Prentice Hall of India 3. Laurene Fausett, "Fundamentals of Neural Networks: Architecture, Algorithms and Applications", Pearson Education, H.J. Zimmerman, "Fuzzy set theory and its Applications", Allied Publishers Ltd. Web resources Prerequisite: NIL

2 Objectives 1. To learn the various architectures of building an ANN and its applications 2. Advanced methods of representing information in ANN like self organizing, associative and competitive learning 3. Fundamentals of Crisp sets, Fuzzy sets and Fuzzy Relations Assessment Details Attendance: 5 marks Cycle test I: 10 marks Cycle test II: 10 marks Surprise test I & II: 5 marks Model Exam: 20marks Test schedule Outcomes S.NO Test Portions Duration 1 Cycle test I Unit I and II 2 periods 2 Cycle test II Unit III and IV 2 periods 3 Model exam Full portions 3 hours Students who have successfully completed this course will have full understanding of the following concepts Course Outcome To learn 1. Fundamentals of Artificial Neural Network 2. Processing elements 3. BPN & CPN architecture 4. Learning algorithms for BPN & CPN 5. Basics of fuzzy set Program outcome 1. An ability to understand Artificial Neural Network concept with the help of Biological Neural network 2. Implement algorithms to train ANN by using learning algorithms 3. To test fuzzy set operations and binary relations

3 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS : Neuro-physiology - General Processing Element - ADALINE - LMS learning rule - MADALINE - XOR Problem - MLP - Back Propagation Network - updation of output and hidden layer weights - application of BPN. Session Name of the Topic Min Ref Teaching Testing No. 1 Neuro-physiology: Biological neural Introduction : Artificial Neural Networks General Processing Element a) Basic building blocks of ANNs b) Terminologies hours method method 1 1 BB Quiz 2 McCulloch-Pitts Neuron Model. 1 1 BB Quiz 3 Learning rules: a) Perceptron b) Delta (LMS) c) Hebb net 4 Adaline and Madaline and its problems 5 Back Propagation : a) Architecture b) Training algorithm c) Selection of parameters d) Learning e) Applications, merits and demerits ASSOCIATIVE MEMORY & CPN: Associative memory - Bi-directional Associative Memory - Hopfield memory - traveling sales man problem Annealing, Boltzmann machine - learning - application - Counter Propagation network -architecture - training - Applications. 6 Associative memory : a) Algorithms for pattern association b) Hetro Associative memory c) Auto Associative memory 7 Bi-directional Associative memory : Architecture, types of nets, application algorithm. 8 Introduction to Feedback Networks: Hopfield nets a) Discrete Hopfield nets b) Continuous Hopfield nets 1 1 BB Test

4 9 Traveling sales man problem and 1 1 BB Test simulated annealing 10 Special Networks: Boltzmann 1 BB Assignment Machine and its learning 11 Counter Propagation Network: a) Fully CPN b) Forward only CPN 1 1 BB Test SELF ORGANIZING MAP & ART: Self-organizing map - learning algorithm - feature map classifier - applications - architecture of Adaptive Resonance Theory - pattern matching in ART network. 12 Self-organizing map : a) Kohonen Self-organizing map b) Learning vector Quantization c) Max and Mexican net d) Hamming net 13 Adaptive Resonance theory: a) Fundamentals b) ART 1 andart2 CRISP SETS AND FUZZY SETS: Introduction - crisp sets an overview - the notion of fuzzy sets -Basic concepts of fuzzy sets - classical logic an overview - Fuzzy logic- Operations on fuzzy sets - fuzzy complement fuzzy union fuzzy intersection combination of operations general aggregation operation. 14 Introduction : Fuzzy logic 2 2 BB Test a) Crisp sets b) Notions of fuzzy sets c) Basic concepts of fuzzy sets d) Classical logic an overview 15 Operations on Fuzzy sets: a) fuzzy complement b) fuzzy union c) fuzzy intersection d) combination of operations e) General aggregation operation. 3 2 BB Test FUZZY RELATIONS Crisp and fuzzy relations - binary relations - binary relations on a single set- equivalence and similarity relations - Compatibility or tolerance relations- orderings - morphisms-fuzzy relation equations 14 Crisp and fuzzy relations 1 2 BB Test 15 Binary relations and binary relations 1 2 BB Test on a single set 16 Equivalence and similarity relations 1 2 BB Test 17 Compatibility or tolerance relations 1 2 BB Test 18 Orderings 1 2 BB Test 19 Morphisms and fuzzy relation equations 2 2 BB Test

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