The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks
| Autor: | Jacek Smoląg, Jarosław Bilski, Alexander I. Galushkin |
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| Rok vydání: | 2014 |
| Předmět: |
Speedup
Quantitative Biology::Neurons and Cognition Artificial neural network business.industry Computer science Deep learning Computer Science::Neural and Evolutionary Computation Rprop Probabilistic neural network ComputingMethodologies_PATTERNRECOGNITION Conjugate gradient method Feedforward neural network Artificial intelligence Types of artificial neural networks business Algorithm |
| Zdroj: | Artificial Intelligence and Soft Computing ISBN: 9783319071725 ICAISC (1) |
| DOI: | 10.1007/978-3-319-07173-2_2 |
| Popis: | This paper presents the parallel architecture of the conjugate gradient learning algorithm for the feedforward neural networks. The proposed solution is based on the high parallel structures to speed up learning performance. Detailed parallel neural network structures are explicitly shown. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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