Fault diagnosis of hydraulic breaking hammer based on Fruit Fly Algorithm optimized fuzzy RBF neural network
Focusing on the variety and uncertainty of the fault reason of a hydraulic breaking hammer ,in order to avoid the problems of traditional fuzzy BP neural network ,such as poor con‐vergence rate in fault diagnosis and easy to fall into a local minimum ,a new method of fault diag‐nosis of hydraulic breaking hammer by using Fruit Fly Algorithm for optimization of fuzzy RBF neural network was proposed .By synthesizing the neural network's associative memory ,processing a‐bility ,and fuzzy logic system's qualitative knowledge ,fuzzy reasoning ability ,optimizing fuzzy RBF neu‐ral network expansion parameter by Fruit Fly Optimization Algorithm ,a relations between the network fault information and fault reasons was established .The simulation tested by MATLAB indicated that fuzzy RBF neural network optimized by Fruit Fly Algorithm worked accurate and fast .The result of the diagnosis agrees with target outputs ,which proves the feasibility of this method .
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