Preference for Reference

Abstract. Predicting the agin life of rubber is mostly based on traditional dynamic methods. These methods often have some limitations, which can not reflect the influence of certain factors. To avoid such limitations, a BP neural network model was established to predict the aging life of rubber. Comparing with the BP neural network model, results from the genetic algorithm optimization model (GA-BP) and the particle swarm optimization model (PSO-BP) showed that the GA-BP network model has better stability and accuracy and can quickly get the global optimal solution. The prediction accuracy of the GA-BP neural network model is better than that of the traditional dynamic model and its result is in good agreement with the experimental data.

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Preference for Reference

Cited by 23 publications

References 11 publications

Australian Academic Librarians' Perceptions of Effective Web 2.0 Tools Used to Market Services and Resources

Australian Academic Librarians' Perceptions of Effective Web 2.0 Tools Used to Market Services and Resources

User preferences related to virtual reference services in an academic library

Prediction of Rubber Aging Life by Intelligent Neural Network

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