DOWNLOAD ALL PREVIOUS YEAR PAPERS HERE of both old and new scheme
download all solved papers here
[url=http://studentsuvidha.com/forum/Thread-compiler-design-solved-papers-mdu-btech][/url]please post some notes here or you can mail at admin@studentsuvidha.com
SYLLABUS:-
Section A
Overview of biological neurons: Structure of biological neurons relevant to ANNs.
Fundamental concepts of Artificial Neural Networks: Models of ANNs; Feedforward & feedback networks; learning rules; Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff learning rule, correction learning rule, Winner –lake all elarning rule, etc.
Section B
Single layer Perception Classifier: Classification model, Features & Decision regions; training & classification using discrete perceptron, algorithm, single layer continuous perceptron networks for linearlyseperable classifications.
Multi-layer Feed forward Networks: linearly non-seperable pattern classification, Delta learning rule for multi-perceptron layer, Generalized delta learning rule, Error back-propagation training, learning factors, Examples.
Section C
Single layer feed back Networks: Basic Concepts, Hopfield networks, Training & Examples.
Associative memories: Linear Association, Basic Concepts of recurrent Auto associative memory: rentrieval algorithm, storage algorithm; By directional associative memory, Architecture, Association encoding & decoding, Stability.
Section D
Self organizing networks: UN supervised learning of clusters, winner-take-all learning, recall mode, Initialisation of weights, seperability limitations
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download all solved papers here
[url=http://studentsuvidha.com/forum/Thread-compiler-design-solved-papers-mdu-btech][/url]please post some notes here or you can mail at admin@studentsuvidha.com
SYLLABUS:-
Section A
Overview of biological neurons: Structure of biological neurons relevant to ANNs.
Fundamental concepts of Artificial Neural Networks: Models of ANNs; Feedforward & feedback networks; learning rules; Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff learning rule, correction learning rule, Winner –lake all elarning rule, etc.
Section B
Single layer Perception Classifier: Classification model, Features & Decision regions; training & classification using discrete perceptron, algorithm, single layer continuous perceptron networks for linearlyseperable classifications.
Multi-layer Feed forward Networks: linearly non-seperable pattern classification, Delta learning rule for multi-perceptron layer, Generalized delta learning rule, Error back-propagation training, learning factors, Examples.
Section C
Single layer feed back Networks: Basic Concepts, Hopfield networks, Training & Examples.
Associative memories: Linear Association, Basic Concepts of recurrent Auto associative memory: rentrieval algorithm, storage algorithm; By directional associative memory, Architecture, Association encoding & decoding, Stability.
Section D
Self organizing networks: UN supervised learning of clusters, winner-take-all learning, recall mode, Initialisation of weights, seperability limitations
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