Coal Mine Gas Prediction Model Based on Particle Swarm Optimization Algorithm

Li Ai; Jia Tang Cheng; Shao Kun Xu

doi:10.4028/www.scientific.net/AMR.546-547.8

Paper Titles

Preface

Coal Mine Gas Prediction Model Based on Ant Colony Neural Network
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Coal Mine Gas Prediction Model Based on Particle Swarm Optimization Algorithm
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Study on Plating Bath Preparation Method of Novel Nano-Composite Plating
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New Electromagnetic Imaging Tool Introduced for Corrosion Detection
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Experiment Study on the Influence Rules of Laser Texturing Micro-Profile on the Surface of 45 Steel
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A New Anode for Photovoltaic Solar Cells
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Flexible Photovoltaic Solar Cells with Nickle Oxide Buffering Layer
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Study on Nickle Oxide Thin Film as Buffering Layer in Photovoltaic Solar Cells
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Coal Mine Gas Prediction Model Based on Particle...

Coal Mine Gas Prediction Model Based on Particle Swarm Optimization Algorithm

Abstract:

For traditional methods for coal mine gas emission prediction accuracy is not high, an adaptive mutation particle swarm optimization neural network approach is introduced. The algorithm increases the mutation operation in iterative process, and adaptive adjusts mutation probability of the size, in order to enhance the ability to jump out of the local optima. The simulation results show that the method can be better predicted coal mine gas, has a certain practicality.

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Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

8-12

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.8

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Online since:

July 2012

Authors:

Li Ai, Jia Tang Cheng, Shao Kun Xu

Keywords:

Adaptive Mutation, Coal Mine Gas Prediction, Neural Network (NN), Particle Swarm Optimization Algorithm (PSO)

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