Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
RELIABILITY PREDICTION OF DRYER BASED ON IMPROVED PSO＿BP NEURAL NETWORK (MT)
When the BP neural network model is used to predict the reliability of the grain dryer, the model has problems such as slow convergence speed and easy to fall into local optimum. An improved particle swarm algorithm is used to optimize the BP neural network model and establish the PSO＿BP neural network The reliability prediction model of grain dryer is compared with MAERMSEMAPE index obtained by BP network model and GA＿BP network model. The research results show that when the improved PSO＿BP network model is used for forecasting, the three indicators are reduced by 0.051 8, 0.047 9 and 28.04% respectively compared with the BP network model; the three indicators are reduced by 0.000 4, 0.000 2 and 0.61% respectively compared with the GA＿BP network model, Which shows that it has smaller errors and better predictive ability. The methods and ideas for realizing accurate prediction of the reliability of grain dryers are provided.
RELIABILITY PREDICTION OF DRYER BASED ON IMPROVED PSO＿BP NEURAL NETWORK (MT)
When the BP neural network model is used to predict the reliability of the grain dryer, the model has problems such as slow convergence speed and easy to fall into local optimum. An improved particle swarm algorithm is used to optimize the BP neural network model and establish the PSO＿BP neural network The reliability prediction model of grain dryer is compared with MAERMSEMAPE index obtained by BP network model and GA＿BP network model. The research results show that when the improved PSO＿BP network model is used for forecasting, the three indicators are reduced by 0.051 8, 0.047 9 and 28.04% respectively compared with the BP network model; the three indicators are reduced by 0.000 4, 0.000 2 and 0.61% respectively compared with the GA＿BP network model, Which shows that it has smaller errors and better predictive ability. The methods and ideas for realizing accurate prediction of the reliability of grain dryers are provided.
RELIABILITY PREDICTION OF DRYER BASED ON IMPROVED PSO＿BP NEURAL NETWORK (MT)
WEN ChangJun (Autor:in) / CHEN Zhe (Autor:in) / SHAO MingYing (Autor:in) / CHEN Li (Autor:in) / XU Yun Fei (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK
| DOAJ | 2021
Network Traffic Prediction Based on Neural Network
| IEEE | 2015
An Improved Technique in Porosity Prediction: A Neural Network Approach
| Online Contents | 1995
An Improved Neural Network Model for Enhancing Rutting Depth Prediction
| DOAJ | 2022
| DOAJ | 2022