Abstract
Single hidden layer feed-forward neural network (SLFN) is one of the most widely used models for intelligent modeling. But the model faces that for small sample sets, the traditional learning algorithm may train a model to fall into the over-fitting sate. In particular, when the dataset contains a large amount of noise, the trained model has weak robustness and is very sensitive to noise. In order to overcome this shortcoming, a robust learning algorithm of SLFN is derived for small and noisy datasets. Due to the introduction of ε-insensitive learning measure and the structural risk term, the proposed algorithm can effectively overcome the shortcoming of the traditional learning algorithm. The experimental results on simulated and real-world datasets also confirm the above advantages.
Original language | Chinese (Simplified) |
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Journal | Kongzhi yu Juece/Control and Decision |
Volume | 27 |
Issue number | 9 |
Publication status | Published - 1 Sept 2012 |
Keywords
- ε-insensitive learning
- Robustness
- Single hidden layer feed-forward neural network
- Structural risk minimization
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Control and Optimization
- Artificial Intelligence