Prediction of Rock Bust Based on Improved FOA-BP Neural Network
LIU Xiaoyue
LI Pengyuan
Abstract：In view of the problem that the dynamic disaster is unpredictable due to the non-linearity and strong coupling in the coal mining process, this paper introduced a Linear Generation Mechanism of candidate Solution( LGMS),Chaotic Search, Particle Swarm Optimization algorithm(PSO) and Simulated Annealing algorithm(SA) to modify Fruit fly algorithm(IFOA), and then by using the capability of searching the global optimal solution of modified FOA, the weight and threshold of BP neural network were adjusted adaptively, a prediction model of rock burst was established. Finally, taking the sample data of Kailuan Coal Mine in Tangshan as an example for simulation verification, the experimental results showed that the accuracy of robustness and measurement are obviously improved, and the network has strong convergence performance and optimization ability.
Keywords：Linear Generation Mechanism of candidate SolutionChaotic SearchParticle Swarm Optimization algorithmSimulated Annealing algorithmBP neural networkrock burstrobustness
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 55-60 )