Course

code	EE717
credit_hours	3
title	Neural Networks and Neurocontrol
arbic title
prequisites	None
credit hours	3
Description/Outcomes	Elementary biophysical background for signal propagation in natural and neural systems. Artificial Neural networks (ANN). Hopfield. Feed forward. Learning techniques of McCulloch and Pitts Model. Connectionist model. The random neural network model. Associative memory. Learning algorithm application to Control engineering.
arabic Description/Outcomes
objectives	The student should be able to: Learn the graduate new techniques in control system. Update the graduate an objective on neural networks and how it is applied in control system.
arabic objectives
ref. books	R. Beale and T. Jackson, “Neural computing: An introduction”, Institute of Physics Publishing, 1990. J. Hertz, A Krogh and R.G. Palmer, “Introduction to the Theory of Neural Computation”, Addison Wesley, Redwood City, CA 1992. M. N. O Ravn, N. K. Hansen, "Neural Networks for Modeling and Control of Dynamic Systems", 2008.
arabic ref. books
textbook
arabic textbook
objective set
content set

Course Content

content serial	Description
1	Introduction.
2	Neuron Model.
3	Perception.
4	Supervised Hebbian learning.
5	Performance Optimization.
6	Widrow – Hoff Learning.
7	Back propagation.
8	Variations on Back Propagation.
9	Associative learning.
10	Associative learning.
11	Competitive Networks.
12	Hopfield Network.
13	Matlab Tool Box.
14	Matlab Tool Box.
15	Matlab Tool Box.
16	Final Exam.