Wavelet Neural Network Structure Optimization Method Based on Grey Relational-Sensitivity
Shiying Pan, Xiuqing Wang
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.100How to use a DOI?
- Multivariate Time Series; Grey Relational-Sensitivity; Hidden Layer Node Number; Wavelet Neural Network
- In view of the wavelet neural network input number to determine the lack of theoretical guidance, the number of hidden layer nodes to determine the defects of difficult, put forward a kind of based on multivariate time series and grey relational - sensitivity of the wavelet neural network structure optimization method. This method firstly before the wavelet neural network learning, number of multivariate time series is used to determine the network's input, and then using grey relation in the process of learning - sensitivity pruning method to determine the neural network hidden layer node number, to achieve the goal of structural optimization. Through the model simulation results in the short-term wind power prediction, the results show that the method the optimized wavelet neural network to improve the wind power prediction accuracy and verified the effectiveness and feasibility of this structure optimization method, for the determination of wavelet neural network structure provides reference.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shiying Pan AU - Xiuqing Wang PY - 2015/06 DA - 2015/06 TI - Wavelet Neural Network Structure Optimization Method Based on Grey Relational-Sensitivity BT - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics PB - Atlantis Press SP - 484 EP - 487 SN - 2352-538X UR - https://doi.org/10.2991/icecee-15.2015.100 DO - https://doi.org/10.2991/icecee-15.2015.100 ID - Pan2015/06 ER -