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Type: Artigo de evento
Title: Using Constructive Learning In Embedded Systems Engineering
Author: Goncalves Rodrigo
Von Zuben Fernando
Gomide Fernando
Abstract: 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.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Date Issue: 1998
Appears in Collections:Unicamp - Artigos e Outros Documentos

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