Dynamic prediction of gas emission based on wavelet neural network toolbox
| Autor: | Yumin Pan, Peng-Qian Xue, Yong-hong Deng, Quanzhu Zhang |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Small data
Scale (ratio) business.industry Feature extraction Energy Engineering and Power Technology Geotechnical Engineering and Engineering Geology Machine learning computer.software_genre Toolbox Wavelet Rate of convergence Feature (computer vision) Sliding window protocol Artificial intelligence business computer Algorithm Geology |
| Zdroj: | Journal of Coal Science and Engineering (China). 19:174-181 |
| ISSN: | 1866-6566 1006-9097 |
| DOI: | 10.1007/s12404-013-0211-7 |
| Popis: | This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network (WNN) toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink