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ECE3009 Pre-requisite Neural Networks and Fuzzy Control ECE2006 - Digital Signal Processing L 3 T P J C 0 0 4 4 Syllabus version 1.0 Course Objectives: 1. To summarize basic learning laws and architectures of neural networks. 2. To describe supervised and unsupervised learning laws of Neural Networks. 3. To introduce Fuzzy Logic, Fuzzy relations and Fuzzy mathematics for designing a Fuzzy logic controller. 4. To discuss neuro fuzzy approaches like ANFIS and CANFIS. Course Outcomes: 1. To translate biological motivations into various characteristics of artificial neural networks 2. To comprehend and analyze basic learning laws of neural networks and activation functions 3. To interpret associative memories for storing and recalling the input patterns 4. To learn and implement supervised and unsupervised learning algorithms for various applications. 5. To learn fuzzification and de-fuzzification methods for developing Fuzzy inference systems 6. To apply and integrate various neuro-fuzzy techniques for designing intelligent systems using ANFIS and CANFIS. 7. To design a model using neural networks and fuzzy logic for various applications. Student Learning Outcomes (SLO) 1,2,5 Module:1 Introduction to Artificial Neural Networks 3 hours Artificial neural networks and their biological motivation, terminology, models of neuron, topology, characteristics of artificial neural networks, and types of activation functions. Module:2 Learning methods 7 hours Error correction learning, Hebbian learning, perceptron – XOR problem– perceptron learning rule convergence theorem – adaline. Module:3 Supervised Learning 9 hours Introduction to ANN architecture, multilayer perceptron, back propagation learning algorithm, momentum factor, radial basis function network. Associative memory: Auto association, hetero association, recall and cross talk. Recurrent neural networks - Hopfield neural network. Module:4 Unsupervised Learning 9 hours Introduction, competitive learning neural networks, max net, Mexican hat, hamming net, Kohonenself organizing feature map, counter propagation, learning vector quantization, adaptive resonance theory, performance of SOM. Module:5 Fuzzy Sets and Fuzzy Relations 4 hours Introduction, classical sets and fuzzy sets, classical relations and fuzzy relations, membership function. Module:6 Fuzzy Inference Systems 6 hours Fuzzification, fuzzy arithmetic, numbers, extension principle, fuzzy inference system, defuzzification, fuzzy rule based systems, fuzzy nonlinear simulation, fuzzy decision making, fuzzy optimization. Module:7 Neuro-Fuzzy Systems 5 hours Introduction, ANFIS, ANFIS as universal approximator, CANFIS. Module:8 Contemporary issues 2 hours Total lecture hours: 45 hours Text Book(s) 1. J.S.R. Jang, C.T. Sun, E. Mizutani, “Neuro Fuzzy and Soft Computing - A computational Approach to Learning and Machine Intelligence”, 2012, 1 st edition, PHI learning Private Limited, New Delhi. 2. Timothy J. Ross, Fuzzy Logic with Engineering Applications, 2016, 4 th edition, John Wiley and sons, USA Reference Books 1. Jacek. M. Zurada, “Introduction to Artificial Neural Systems”, 2014, 11 th edition, Jaico Publishing House, Mumbai. 2. Simon Haykin, “Neural Networks and Learning Machines”, 2016, 3 rd edition, Pearson Education Inc. India 3. Samir Roy, Udit Chakraborthy, “Introduction to Soft Computing Neuro - Fuzzy and Genetic Algorithms”, 2013, 1 st edition, Pearson education, Noida. Mode of Evaluation:Internal Assessment (CAT, Quizzes, Digital Assignments) & Final Assessment Test (FAT) Typical Projects 1. Adaptive filtering for Medical (ECG) signals. 2. Adaptive Neuro Fuzzy Inference System 3. Automation of Traffic signal using Raspberry Pi 4. Cardiac Image Diagnostic System 5. Cryptographic System using Neural Networks 6. Design and Development of Biometric Recognition and Matching System 7. Digital Audio Watermark Embedding System 8. Electrical load forecasting using Neural Networks 9. Electronic Music System using ANN 10. Face Identification System using ANN 11. Feature Extraction of EEG Signals 12. Image Decryption using Neural Networks 13. Internal Fault identification using Artificial Neural Network 14. Signature Forgery and Handwriting Detection System 15. Smart Driver Assist System using Raspberry Pi 16. Speaker Recognition using Soft Computing 17. Speech Separation Using ICA Based Neural Networks Mode of evaluation:Review I, Review II and Review III Recommended by Board of Studies 13/06/2015 Approved by Academic Council No. 37 Date 16/06/2015

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