Mine Forecast Based on Genetic Annealing Neural Network

Shao, Yuxiang; Zhang, Dongmei

doi:10.1109/itcs.2009.54

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2017

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“…These methods include autoregressive moving average model [1] [2] , chaos and fractal [3][4] [5] , neural network [6][7] [8] , support vector machine [9] [10] , data mining and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on Time Series of Gas Trend Based on R Language

Wang¹,

Li²,

Chang³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Use the history monitoring data of the gas of Shaanxi Huangling No.2 coal mine, the time series of monitoring data are decomposed and smoothed by using R language. The abnormal and missing values in the raw data are analyzed and three methods of time series smoothing (Simple exponential smoothing method, Holt exponential smoothing method and Holt-Winters exponential smoothing method) are used to predict the variation law of gas concentration. The actual value and the predicted value are compared to verify the effectiveness of the forecasting method, and the conclusion can make practical significance for the safe production of the mine.

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“…These methods include autoregressive moving average model [1] [2] , chaos and fractal [3][4] [5] , neural network [6][7] [8] , support vector machine [9] [10] , data mining and so on.…”

Section: Introductionmentioning

confidence: 99%