Neural Networks and Fuzzy Systems detailed syllabus scheme for Electronics Engineering (EL), 2019-20 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 6th Sem Scheme of Electronics Engineering (EL), 2019-20 Onwards, do visit EL 6th Sem Scheme, 2019-20 Onwards. For the Open Elective-I Labs scheme of 6th Sem 2019-20 onwards, refer to EL 6th Sem Open Elective-I Labs Scheme 2019-20 Onwards. The detail syllabus for neural networks and fuzzy systems is as follows.
Neural Networks and Fuzzy Systems Syllabus for Electronics Engineering (EL) 3rd Year 6th Sem 2019-20 DBATU
Course Objectives:
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Course Outcomes:
- The student will be able to obtain the fundamentals and types of neural networks.
- The student will have a broad knowledge in developing the different algorithms for neural networks.
- Student will be able analyze neural controllers.
- Student will have a broad knowledge in Fuzzy logic principles.
- Student will be able to determine different methods of Deffuzification.
UNIT – 1 Introduction
Biological neurons, McCulloch and Pitts models of neuron, Types of activation function, Network architectures, Knowledge representation, Learning process: Error-correction learning, Supervised learning, Unsupervised learning, Learning Rules.
UNIT – 2 Single Layer Perception
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Unit – 3 Multilayer Perception
Derivation of the back-propagation algorithm, Learning Factors.
UNIT – 4 Radial Basis and Recurrent Neural Networks
RBF network structure theorem and the reparability of patterns, RBF learning strategies, K-means and LMS algorithms, comparison of RBF and MLP networks, Hopfield networks: energy function, spurious states, error performance.
UNIT – 5 Neuro-dynamics
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UNIT – 6 Fuzzy logic
Fuzzy sets, Properties, Operations on fuzzy sets, Fuzzy relation Operations on fuzzy relations, The extension principle, Fuzzy mean Membership functions, Fuzzification and defuzzification methods, Fuzzy controllers.
Reference Books:
- Simon Haykin, “Neural Network a – Comprehensive Foundation”, Pearson Education.
- Dr. S. N. Sivanandam, Mrs S.N. Deepa Introduction to Soft computing tool Wiley Publication.
- Satish Kumar Neural Networks: A classroom Approach Tata McGraw-Hill.
- Zurada J.M., “Introduction to Artificial Neural Systems, Jaico publishers.
- Thimothv J. Ross, “Fuzz V Logic with Engineering Applications”, McGraw.
- Ahmad Ibrahim, “Introduction to Applied Fuzzy Electronics’, PHI.
- Rajsekaran S, VijaylakshmiPai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, PHI.
- Hagan, Demuth, Beale, eNeural Network Design!, Thomson Learning
- Christopher M Bishop Neural Networks for Pattern Recognition, Oxford Publication.
- William W Hsieh Machine Learning Methods in the Environmental Sciences Neural Network and Kernels Cambridge Publication.
- Dr. S. N. Sivanandam, Dr. S. Sumathi Introduction to Neural Network Using Matlab Tata McGraw-Hill
For detail syllabus of all subjects of Electronics Engineering (EL) 6th Sem 2019-20 onwards, visit EL 6th Sem Subjects of 2019-20 Onwards.