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- W2368704704 abstract "It was held by the authors that in order to predict the amount of gas emitted more accurately,and then to prevent the occurrence of gas disasters,a chaotic time series prediction model based on the improved extreme learning machine(IELM) should be built and used.For this end,the multivariate time series consisted of the gas emission monitoring data were reconstructed using phase space.The delay time and the best embedding dimension for each variable were obtained using the mutual information method and false neighbor.The optimal learning parameters from network input layer to hidden layer were calculated using the least-square method and error feedback principle.The ELM was so improved.lastly,a gas chaotic time series prediction model was built with the help of IELM.Through the simulation experiment,the prediction model has predicted that the maximum relative error is 3.290 2 %,the minimum relative error is 0.898 2 % and the average relative error is 1.952 8 %." @default.
- W2368704704 created "2016-06-24" @default.
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- W2368704704 date "2012-01-01" @default.
- W2368704704 modified "2023-09-22" @default.
- W2368704704 title "Prediction of Chaotic Time Series of Gas Emission Based on Improved Extreme Learning Machine" @default.
- W2368704704 hasPublicationYear "2012" @default.
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