JNTUH B.Tech 4th year (4-2) Neural Networks and Fuzzy Logic gives you detail information of Neural Networks and Fuzzy Logic (Elective – III) R13 syllabus It will be help full to understand you complete curriculum of the year.
Objective
This course introduces the basics of Neural Networks and essentials of Artificial Neural Networks with Single Layer and Multi layer Feed Forward Networks. Also deals with Associate Memories and introduces Fuzzy sets and Fuzzy Logic system components. The Neural Network and Fuzzy Network system application to Electrical Engineering is also presented. This subject is very important and useful for doing Project Work.
UNIT-I
Introduction & Essentials to Neural Networks: Introduction, Humans and Computers, Organization of the Brain, Biological Neuron, Biological and Artificial Neuron Models, Hodgkin-Huxley Neuron Model, Integrate-and-Fire Neuron Model, Spiking Neuron Model, Characteristics of ANN, McCullochP iUs Model, Historical Developments, Potential Applications of ANN. Artificial Neuron Model, Operations of Artificial Neuron, Types of Neuron Activation Function, ANN Architectures, Classification Taxonomy of ANN — Connectivity, Neural Dynamics (Activation and Synaptic), Learning Strategy (Supervised, Unsupervised, Reinforcement), Learning Rules, Types of Application
UNIT—II
Single & Multi Layer Feed Forward Neural Networks : Introduction, Perceptron Models: Discrete, Continuous and Multi-Category, Training
Algorithms: Discrete and Continuous Perceptron Networks, Perceptron Convergence theorem, Limitations of the Perceptron Model, Applications. Credit Assignment Problem, Generalized Delta Rule, and Derivation of Back-propagation (BP) Training, Summary of Back-propagation Algorithm, Kolmogorov Theorem, Learning Difficulties and Improvements.
UNIT-III
Associative Memories-I: Paradigms of Associative Memory, Pattern Mathematics, Hebbian Learning, General Concepts of Associative Memory (Associative Matrix, Association Rules, Hamming Distance, The Linear Associator, Matrix Memories, Content Addressable Memory)
TEXT BOOKS
- Neural Networks, Fuzzy logic, Genetic algorithms: synthesis and applications, Rajasekharan and Pal, PHI.
- Neural Networks and Fuzzy Logic, C. Naga Bhaskar, G. Vijay Kumar, BS Publicatior-is.
REFERENCE BOOKS
- Artificial Neural Networks, B. Yegnanarayana, PHI.
- Artificial Neural Networks, Zaruda, PHI.
- Neural Networks and Fuzzy Logic System, Bail Kosko, PHI.
- Fuzzy Logic and Neural Networks, M. Amirthavalli, Scitech Publications India Pvt. Ltd.
- Neural Networks, James A Freeman and Davis Skapura, Pearson Education.
- Neural networks by satish Kumar, TIVIH, 2004
- Neural Networks, Simon Hakins , Pearson Education.
- Neural Engineering, C.Eliasmith and CH.Anderson, PHI.
Outcome
After going through this course the student gets a thorough knowledge on, biological neurons and artificial neurons, comparative analysis between human and computer, artificial neural network models, characteristics of ANN’s, different types of activation functions, learning strategies, learning rules, perceptron models, single and multi layer feed-forward and feed—back neural networks, back-propagation algorithm, Kolmogorov Theorem, different types of associative memories and basics of fuzzy logic, concept of classical and fuzzy sets, fuzzy logic system components fuzzification and defuzzification, with which he/she can able to apply the above conceptual things to real-world electrical and electronics problems and applications.
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