Vishwakarma Institute of Technology B.E. (Electronics)
Vishwakarma Institute of Technology B.E. (Electronics) Vishwakarma Institute of Technology B.E. (Electronics)
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Electronics Engineering FF No. : 654 EC42110:: ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC Credits: 03 Teaching Scheme: - Theory 3 Hrs/Week Prerequisites: nil Objectives: 1. To give overview of Neural network systems and standards across the globe. 2. To create awareness in the current trends and technologies in Neural networks 3. To learn different neural Networks. 4. Mapping with PEO : 1,2,4,5,6,7,8,9 Unit_1 Introduction to ANN :- (04Hrs) PART ( A) History of Neural networks, Neural net architecture, Neural learning, Evaluation of networks, Implementation. PART (B) Applications of neural networks. Unit_2 Supervised Learning (10Hrs) PART (A) Perceptions , Linear separability, preceptron training algorithms, modifications, Support vector machines, Multilevel discrimination, back propagation algorithm. Adaptive multilayer networks, predication networks, Polynomial Networks. PART (B) Radial basis functions, probabilistic networks. UNIT _3 Unsupervised & Associative Learning (12Hrs) PART (A) Winner-Takes –All network, Learning vector quantization, counter propagation networks, Adaptive Resonance theory, Topological Organized networks ,Distance based learning, Max Net, Competitive Net. Associative non iterative procedures for association, Hop field networks ,Optimization, Learning using Hopfield networks, Brain state in a box network. PART (B) Principal Component Analysis, Boltzman machines, Hetero-associators UNIT _4 Evolutionary Optimization :- (08Hrs) PART (A) Optimization and search, Evolutionary Computation, Evolutionary Algorithms for training neural networks, Learning connection weights, Learning architectures. PART (B) Hybrid evolutionary Approaches. Structure & Syllabus of B.E (Electronics) Program – Pattern ‘C11’, Rev01, dt. 2/4/2011 75
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Electronics Engineering UNIT _5 Fuzzy Logic (06Hrs) PART (A) Fuzzy sets and fuzzy rules, Fuzzy relations, Properties of Fuzzy sets, Fuzzy graphs , Fuzzy numbers, Functions with Fuzzy arguments, Arithmetic operations on fuzzy numbers. PART(B) Applications of fuzzy logic. Text books 1. Elements of Artificial Neural Networks - by Kishan Mehrotra, Chilukurik. Mohan, Sanjay Ranka Penram International Publishing (India) Pvt. Ltd. Second edition, 2. Fuzzy Logic by John Yen,Reza Langari, Pearson Educations, First edition. Reference books 1. Neural Network and Fuzzy system by Bart Kosko, John c. Burgess. 2. Fundamental of Artificial Neural Networks. By M.H. Hassoun. 3. Introduction to Artificial Neural Network system by M.Zurada. 4. Relevant IEEE Papers. Structure & Syllabus of B.E (Electronics) Program – Pattern ‘C11’, Rev01, dt. 2/4/2011 76
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BRACT’S<br />
<strong>Vishwakarma</strong> <strong>Institute</strong> <strong>of</strong> <strong>Technology</strong>, Pune – 411 037<br />
Department <strong>of</strong> <strong>Electronics</strong> Engineering<br />
FF No. : 654<br />
EC42110:: ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC<br />
Credits: 03<br />
Teaching Scheme: - Theory 3 Hrs/Week<br />
Prerequisites: nil<br />
Objectives:<br />
1. To give overview <strong>of</strong> Neural network systems and standards across the globe.<br />
2. To create awareness in the current trends and technologies in Neural networks<br />
3. To learn different neural Networks.<br />
4. Mapping with PEO : 1,2,4,5,6,7,8,9<br />
Unit_1 Introduction to ANN :-<br />
(04Hrs)<br />
PART ( A)<br />
History <strong>of</strong> Neural networks, Neural net architecture, Neural learning, Evaluation <strong>of</strong><br />
networks, Implementation.<br />
PART (B)<br />
Applications <strong>of</strong> neural networks.<br />
Unit_2 Supervised Learning<br />
(10Hrs)<br />
PART (A)<br />
Perceptions , Linear separability, preceptron training algorithms, modifications,<br />
Support vector machines, Multilevel discrimination, back propagation algorithm.<br />
Adaptive multilayer networks, predication networks, Polynomial Networks.<br />
PART (B)<br />
Radial basis functions, probabilistic networks.<br />
UNIT _3 Unsupervised & Associative Learning<br />
(12Hrs)<br />
PART (A)<br />
Winner-Takes –All network, Learning vector quantization, counter propagation<br />
networks, Adaptive Resonance theory, Topological Organized networks ,Distance based<br />
learning, Max Net, Competitive Net. Associative non iterative procedures for association,<br />
Hop field networks ,Optimization, Learning using Hopfield networks, Brain state in a<br />
box network.<br />
PART (B)<br />
Principal Component Analysis, Boltzman machines, Hetero-associators<br />
UNIT _4 Evolutionary Optimization :-<br />
(08Hrs)<br />
PART (A)<br />
Optimization and search, Evolutionary Computation, Evolutionary Algorithms for<br />
training neural networks, Learning connection weights, Learning architectures.<br />
PART (B)<br />
Hybrid evolutionary Approaches.<br />
Structure & Syllabus <strong>of</strong> B.E (<strong>Electronics</strong>) Program – Pattern ‘C11’, Rev01, dt. 2/4/2011<br />
75
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