SCHEME/SYLLABUS
: MCA(SE)
(Fifth Semester)
Code
No: IT 821
Subject: Neural Networks
Biolotical, Analogy, Architecture classification, Neural Models, Learning Paradigm and Rule, single unit mapping and the preception.
Feed forward networks – Review of optimization methods, back propagation, variation on backpropagation, FFANN mapping capability, Mathematical properties of FFANN’s Generalization, Bios & variance Dilemma, Radiol Basis Function networks.
Recurrent Networks – Symmetric hopfield networks and associative memory, Boltzmann machine, Adaptive Resonance Networks
PCA, SOM, LVQ, Hopfield Networks, Associative Memories, RBF Networks, Applications of Artificial Neural Networks to Function Approximation, Regression, Classification, Blind Source Separation, Time Series and Forecasting.
Text / Reference:
1. Haykin S., “Neural Networks-A Comprehensive Foundations”, Prentice-Hall International, New Jersey, 1999.
2. Anderson J.A., “An Introduction to Neural Networks”, PHI, 1999.
3. Hertz J, Krogh A, R.G. Palmer, “Introduction to the Theory of Neural Computation”,
4. Addison-Wesley, California, 1991.
5. Hertz J, Krogh A, R.G. Palmer, “Introduction to the Theory of Neural Computation”, Addison-Wesley, California, 1991.
6. Freeman J.A., D.M. Skapura, “Neural Networks: Algorithms, Applications and Programming Techniques”, Addison-Wesley, Reading, Mass, (1992).
7. Golden R.M., “Mathematical Methods for Neural Network Analysis and Design”, MIT Press, Cambridge, MA, 1996.
8. Cherkassky V., F. Kulier, “Learning from Data-Concepts, Theory and Methods”, John Wiley, New York, 1998.
9. Anderson J.A., E. Rosenfield, “Neurocomputing: Foundatiions of Research, MIT Press, Cambridge, MA, 1988.
10. Kohonen T., “Self-Organizing Maps”, 2nd Ed., Springer Verlag, Berlin, 1997.
11 Patterson D.W., “Artificial Neural Networks: Theory and Applications”, Prentice Hall, Singapore, 1995.
10. Vapnik V.N., “Estimation of Dependencies Based on Empirical Data”, Springer Verlag, Berlin, 1982.
11. Vapnik V.N., “The Nature of Statistical Learning Theory”, Springer Verlag, New York, 1995.
12. Vapnik V.N., “Statistical Learning Theory: Inference from Small Samples”, John Wiley, 1998.