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- W2359530088 abstract "In view of the predication difficulty of yarn breaking strength in the process of complex spinning,aprediction method based on particle swarm optimization(PSO)is put forward to optimize the yarn breaking strength of BP neural network.This method uses the PSO to optimize weights and threshold of neural network,improve convergence speed of neural network and obtain the ability of global optimal solution.A lot of field quality testing data in spinning workshop is taken as the objects to conduct the prediction verification.The results show that the prediction correlation(the degree of consistency between the actual values and estimated values)of PSO-BP neural network has been improved by 5.0% and 4.6%respectively compared with the traditional BP algorithm and GA-BP algorithm.Besides,it has higher prediction precision than BP neural network and GA-BP neural network." @default.
- W2359530088 created "2016-06-24" @default.
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- W2359530088 date "2015-01-01" @default.
- W2359530088 modified "2023-09-23" @default.
- W2359530088 title "Combining the Particle Swarm Optimization with BP Neural Network for Yarn Quality Forecasting" @default.
- W2359530088 hasPublicationYear "2015" @default.
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