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- W2140162799 abstract "Embedded systems differ from many other 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 the 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, the constructive training method is reviewed, a practical application addressed and the results obtained discussed." @default.
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- W2140162799 date "2002-11-27" @default.
- W2140162799 modified "2023-10-02" @default.
- W2140162799 title "Using constructive learning in embedded systems engineering" @default.
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- W2140162799 doi "https://doi.org/10.1109/ijcnn.1998.682272" @default.
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