Title :
Predicting the Amount of Gas Emitted Based on Wavelet Neural Network
Author :
Pengqian, Xue ; Xiaoyu, Zhang
Author_Institution :
Dept. of Electron. Inf., North China Inst. of Sci. &Technol., Beijing, China
Abstract :
Precisely predicting the amount of gas emitted from the mine, which is a matter of nonlinear model related to many factors such as nature characters and mining technology, is of great importance in the design of mine and production safety. As back-propagation neural networks (BPNN) have the shortcomings of slow convergence and being prone to fall into local optimums, a new method of wavelet neutral network which can make full use of its time-frequent characteristics and combination with the self-study ability of neutral networks is presented to form a model for predicting the amount of gas emitted from the mine. Based on this model, using Matlab6.5, the simulator of wavelet and BP neural network is designed. The simulation results obtained show that the new method can achieve faster convergence and more accurate prediction compared with that of BPNN.
Keywords :
convergence; mining industry; neural nets; prediction theory; production; safety; time-frequency analysis; wavelet transforms; Matlab6.5; back propagation neural network; convergence; gas amount prediction; mine safety; mining technology; nonlinear model; production safety; time frequent characteristic; wavelet neural network; Artificial neural networks; Electronic mail; Feedforward neural networks; Mathematical model; Modeling; Neural networks; Nonlinear dynamical systems; Prediction methods; Predictive models; Wavelet analysis; gas emission quantity; nonlinear; predicting; wavelet neural network;
Conference_Titel :
Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-0-7695-3728-3
DOI :
10.1109/CASE.2009.83
