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2004 | OriginalPaper | Buchkapitel

Genetic Generation of High-Degree-of-Freedom Feed-Forward Neural Networks

verfasst von : Yen-Wei Chen, No firstname given Sulistiyo, Zensho Nakao

Erschienen in: Advances in Neural Networks – ISNN 2004

Verlag: Springer Berlin Heidelberg

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Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This is particularly important when the underlying model of the data is unknown. The proposed algorithm is intended to develop automatically an appropriate neural network (including the number of layers, the number of processing elements per layer, and types of each processing element) needed to solve the given problem. Genetic programming (GP) is used to develop the neural network’s structure and the resilient-back-propagation (RPROP) will be used to train the neural network.

Metadaten
Titel
Genetic Generation of High-Degree-of-Freedom Feed-Forward Neural Networks
verfasst von
Yen-Wei Chen
No firstname given Sulistiyo
Zensho Nakao
Copyright-Jahr
2004
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-540-28647-9_32

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