Neural Networks

Code

10992

Academic unit

Faculdade de Ciências e Tecnologia

Department

Departamento de Engenharia Electrotécnica

Credits

6.0

Teacher in charge

José Barahona da Fonseca

Weekly hours

4

Teaching language

Português

Objectives

Learn the fundamental concepts and techniques of 'Computational Neuroscience' and of ANNs and learn to solve engineering problems with ANNs.

Subject matter

1. The Natural Neuron,1.1 The Hodgkin & Huxley's Model; 1.2 The Wilson's Model, 2. Introduction to ANNs, 3. The Rosenblatt's Perceptron, 4. The  Rosenblatt's Perceptron Learning Rule, 5. Limitations of One Layer Perceptrons,  6. Sigmoidal Networks, 7. The Backpropagation Algorithm, 8. Variants of Backpropagation Algorithm, 9. Introdution to Recorrent Networks. Learning in Recurrent Networks,            10. Hopfield Recurrent Network. The Hopfield Network as  Associative Memory. Stability Analysis, 11. Illustrative Examples of Applications of Recurrent Neural Networks.

Bibliography

1. M. T. Hagan, H. B. Demuth and  M. Beale,
 Neural Network Design,
1996, PWS PUBLISHING COMPANY, Boston, MA.

2. T. P. Trappenber,
Fundamentals of Computational Neuroscience,
2002, Oxford University Press, NewYork.

Evaluation method

5 Individual Practical Reports where are proposed the solution of problems that imply the profound knowledge of all the program of Neural Networks

Courses

• Electrical and Computer Engineering