Tuesday, December 11, 2012

Neural Network and Expert System : [BE - IT]

 
  The subject "Neural Network and Expert System" is introduced as an elective subject in the final semester for BE -IT. The number of students who opt for this subject are very few compared to those opting GIS. The major reason why this subject  is not chosen as an elective subject is because it requires Artificial Intelligence as a pre-requisite. Moreover, the subject is a bit confusing and time consuming unlike 'Artificial Intelligence'. Students have managed to score averagely in this subject. And there have been rumors about a local author copy from Nirali Publications for this subject, but no confirm news - as I have not seen the copy myself!


Lets have a look at the syllabus for the subject :


Unit I :
Introduction to Artificial Neural Networks
Biological Neural Networks, Pattern analysis tasks: Classification and Clustering, Computational models of neurons, Basic structures and properties of Artificial Neural Networks, Structures of Neural Networks Learning principles

Unit II 
Feedforward Neural Networks
Perceptron, its learning law , Pattern classification using perceptron, Single layer and Multilayer feed forward Neural Networks (MLFFNNs), Pattern classification and regression using MLFFNNs, ADALINE : The Adaptive Linear Element, its Structure and Learning laws, Error back propagation learning, Fast learning methods: Conjugate gradient method, Auto associative Neural Networks, Bayesian Neural Networks

Unit III 
Radial Basis Function Networks and Pattern Analysis
Regularization theory, RBF networks for function approximation , RBF networks for pattern classification
Kernel methods for pattern analysis: Statistical learning theory, Support vector machines for pattern classification, Relevance vector machines for classification.

Unit IV 
Self organizing maps and feedback networks
Pattern clustering,, Topological mapping, Kohonen’s self, organizing map Feedback Neural Networks : Pattern storage and retrieval ,Hopfield model, Boltzmann machine, Recurrent Neural Networks

Unit V 
Expert Systems Architectures:
Introduction, Rule Based System Architecture, Non-Production System Architecture, Dealing with uncertainty, Knowledge Acquisition and Validation

Unit VI 
Shells and Case Studies
Expert System Shells , Knowledge System Building Tools for Expert System, Expert System tools case study – MYCIN – EMYCIN -ELIZA Knowledge Management (Wiki Web case study)





Download E-Books for
 Neural Networks & Expert System


An Introduction to Neural Networks
James Anderson




File Type : DJVU
File Size : 5MB


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Artificial Intelligence & Expert Systems for Engineers
Krishnamoorty, Rajeev



File Type :  PDF
File Size : 3.5 MB



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Pattern Recognition & Machine Learning
C. S. Bishop



File Type : PDF
File Size : 4.2 MB



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Artificial Neural Network
Colin Fyfe



File Type : DJVU
File Size : 1.17MB



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Artificial Neural Network 
An Introduction to ANN Theory & Practice
P. J. Braspenning



File Type : DJVU
File Size : 2.1 MB



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