Application of BP Network Based on PSO Algorithm in Cementing Quality Prediction

Abstract:

Because of the defect in traditional BP network of cementing quality prediction at present which is sensitive with the initial weights, easy to fall into the local least value,low forecast precision and slow convergence speed occurred. In order to overcome the shortcomings of traditional BP network, the paper introduced the particle swarm optimization (PSO) algorithm based on the random global optimization into the neural network training. The PSO is used to optimize weights of BP network. The simulation results show that this method has shorter training time and higher prediction accuracy than the BP network, and it can improve cementing quality and realize prediction and tracking analysis of cementing quality. It has good serviceability for predicting all kinds of information not known in cementing. It has provided a new method for cementing quality prediction.

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 4433-4436
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.4433
• Online since: May 2014
• Authors: Hong Mei Ni, Zhian Yi, Pan Chi Li, Xi Feng Tong
• Keywords: BP Network, Cementing Quality, Particle Swarm Optimization Algorithm

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