JNTUH B.Tech 4th year (4-2) Artificial Neural Networks gives you detail information of Artificial Neural Networks (Elective – III) R13 syllabus It will be help full to understand you complete curriculum of the year.
UNIT- I
Introduction – what is a neural network? Human Brain, Models of a Neuron, Neural networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks Learning Process — Error Correction learning, Memory based learning, Hebbian learning, Competitive, Boltzmann learning, Credit Assignment Problem, Memory, Adaption, Statistical nature of the learning process.
UNIT-II
Back Propagation: back propagation and differentiation, Hessian matrix, Generalization, Cross validation, Network pruning Techniques, Virtues and limitations of back propagation learning, Accelerated convergence, supervised learning.
UNIT- III
Single Layer Perceptions: Adaptive filtering problem, Unconstrained Organization Techniques, Linear least square filters, least mean square algorithm, learning curves, Learning rate annealing techniques, perceptron — convergence theorem, Relation between perceptron and Bayes classifier for a Gaussian Environment Multilayer Perceptron — Back propagation algorithm XQR problem, Heuristics, Output representation and decision rule, Computer experiment, feature detection.
TEXT BOOK
- Neural networks: A comprehensive foundation/ Simon Hhaykin/ PHI.
REFERENCES
- Artificial neural networks! B.Vegnanarayana/PHI.
- Neural networks in Computer intelligence! Li Mm Fu/ TMH/2003.
- Neural networks! James A Freeman David M S kapura/ Pearson ed ucation/2004.
- Introduction to Artificial Neural Systems/Jacek M. Zurada/JAICO Publishing House Ed. 2006.
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