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.

Cementing Quality Prediction in the Shunbei Block Based on Genetic Algorithm and Support Vector Regression

“…This method was used to improve defects, which include the slow convergence speed of the traditional BP network in cementing quality prediction. This work reduced the training time and improved the accuracy of cementing quality prediction, but it was not time-effective [16].…”

Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

“…The considered problem is how accurate and fast can the weights of NN be determined by BP and PSO to learn a common function. Another research that compared the PSO and BP for optimization NN was proposed by Ni et al [25] in 2014. They introduced PSO for stochastic global optimization in NN training to solve the flaws of the traditional BP network in cementing prediction.…”