Name
Title | ||
|---|---|---|
Speed up training of the recurrent neural network based on constrained optimization techniques |
Abstract | ||
|---|---|---|
In this paper, the constrained optimization technique for a substantial problem is explored, that is accelerating training
the globally recurrent neural network. Unlike most of the previous methods in feedforward neural networks, the authors adopt
the constrained optimization technique to improve the gradientbased algorithm of the globally recurrent neural network for
the adaptive learning rate during training. Using the recurrent network with the improved algorithm, some experiments in two
real-world problems, namely, filtering additive noises in acoustic data and classification of temporal signals for speaker
identification, have been performed. The experimental results show that the recurrent neural network with the improved learning
algorithm yields significantly faster training and achieves the satisfactory performance. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/BF02951621 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
recurrent neural network,neural network,feedforward neural network,constrained optimization | Feedforward neural network,Computer science,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Machine learning,Constrained optimization | Journal |
Volume | Issue | ISSN |
11 | 6 | 1860-4749 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Chen | 1 | 750 | 60.37 |
| Weiquan Bao | 2 | 0 | 0.34 |
| Huisheng Chi | 3 | 211 | 22.81 |
| 陈珂 | 4 | 2 | 0.71 |
| 包威权 | 5 | 0 | 0.34 |