Fault Diagnosis Method for Hydraulic Cylinder Based on Adaptive Mutation Particle Swarm Optimized BP Neural Network
This paper innovatively explores a hydraulic cylinder fault diagnosis method that integrates adaptive mutation particle swarm opti-mization and BP neural network.In contrast to the conventional PSO-BP neural network model,we introduce a mutation operation,drawing inspiration from the genetic algorithm's concept,allowing particles to break away from previously discovered optimal positions and conduct more extensive searches.The introduction of this mutation operation expands the search space,enhances the algorithm's potential to find better solutions,and effectively improves the efficiency of the BP neural network hydraulic cylinder fault diagnosis model.
NETL
NSTL
万方数据
