Actas de congresos
Using Constructive Learning In Embedded Systems Engineering
Registro en:
Ieee International Conference On Neural Networks - Conference Proceedings. Ieee, Piscataway, Nj, United States, v. 1, n. , p. 251 - 255, 1998.
2-s2.0-0031638408
Autor
Goncalves Rodrigo
Von Zuben Fernando
Gomide Fernando
Institución
Resumen
Embedded systems differ from many others engineering applications in two essential requirements: they are usually restricted to use slow processors, and they must fit within a reduced amount of memory. One of the main claims within neural networks field is that once trained, they are very fast to process. However, many neural network structures need a respectable amount of memory to maintain their information. This paper shows how constructive learning methods can be used to gradually increase a feedforward neural network complexity to achieve an optimal trade-off between the desired training error and memory requirements. This is a very important issue in engineering design tasks and applications, especially for embedded systems. In addition, a constructive training method is reviewed, a practical application addressed and the results obtained discussed. 1
251 255