Abstract
In the coal production, gas outburst is a major threat of natural disasters; it caused huge casualties in China. There are also many patents about gas outburst. In order to predict gas outburst, we use a new intelligent algorithm (opposite degree algorithm). The opposite degree algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. The experiment is conducted based on real coal mine data in China. By learning 18 groups of gas outburst data, forecasts the results of 7 groups of gas outburst data. The accuracy of experiment is 6/7. Meanwhile, we introduce the prediction experiments based on single index method and comprehensive index method and BP neural network for the result comparison. Results show that opposite degree method is as good as the BP neural network method or comprehensive index method. Gas outburst prediction based on opposite degree algorithm proved the algorithm is feasible and effective, and can be used in value prediction.
Keywords: Opposite degree, gas outburst, prior value, posteriori value.
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
23
2
1