Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound

Abstract

Genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the back propagation（BP） neural network to establish the prediction model of Mooney viscosity of ethylene-propylene-diene monomer (EPDM) compound, and the error of the prediction results was compared and analyzed. The results showed that the predicted va-lues of the BP neural network model optimized by the two algorithms all maintained a high degree of fit and correlation with the measured values. Compared with the single BP neural network, the accuracy of the GA-BP neural network prediction model increased by 58.9% and the accuracy of the PSO-BP model increased by 3.57%, which indicated that the prediction accuracy of the prediction model optimized by the two algorithms, especially the BP neural network prediction model optimized by GA, improved significantly Mooney viscosity of EPDM compound.

Keywords

• back propagation neural network
• genetic algorithm
• particle swarm optimization
• ethylene-propylene-diene monomer
• compound
• mooney viscosity
• forecast model