An Efficient Generalized Single Hidden Layer Neural Networks Based on Conjugate Gradient Method
[1]Sun Feng,Gong Xiaoling,Zhang Bingjie,et al.An Efficient Generalized Single Hidden Layer Neural Networks Based on Conjugate Gradient Method[J].Journal of Zhengzhou University (Engineering Science),2018,39(02):28-32.[doi:10.13705/j.issn.1671-6833.2017.05.011]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39
Number of periods:
2018 02
Page number:
28-32
Column:
Public date:
2018-03-30
- Title:
- An Efficient Generalized Single Hidden Layer Neural Networks Based on Conjugate Gradient Method
- CLC:
- -
- Abstract:
- The single hidden layer feedforward neural network was efficient with simple structure. Back Propagation Error (BP) algorithm was one of its typical learning algorithm which had one main shortcoming of the slow learning speed because of the use of the steepest descent method. Extreme Learning Machine (ELM) which could greatly accelerate the learning speed of networks was put forward. However, it demanded much more hidden neurons than BP algorithm to get the match accuracy, which led to redundant structure of networks and more testing time. Motived by the USA (Upper-layer-Solution-Aware）) which was a combination of the steepest descent method and ELM, in this paper, we proposed an algorithm based on the conjugate gradient method and train the network on different data sets. The Simulation results showed our algorithm had a better performance than USA and ELM with the same structure of the network.
- References:
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- Similar References:
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Last Update:
2018-04-02