Acceleration Compensating Fuzzy Control in Magnetic Suspension System

Liu, He Xiang; Yu, Haitao; Hu, Min; Huang, Lei; Yu, Li

doi:10.4028/www.scientific.net/amr.468-471.64

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The artificial neural network (ANN) developed in recent years has very high nonlinear mapping capability. Through learning, the ANN can approach to the researched object at discretion precision, which has high efficiency for faults isolation; at the same time, the ANN theory has many advantages such as self-study, self-organize, self-adaptation and self-correction, ANN has been used in many domains successfully [1][2].…”

Section: Introductionmentioning

confidence: 99%

Application of ANN in Hydraulic Pressure Control Fault Diagnosis System

Zhang

Ding

2013

AMM

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The existing hydraulic pressure control fault diagnosis system is effective on fault detection, but the fault isolation capability is bad. In order to improve the capability of the fault isolation, the artificial neural network (ANN) is used in the fault diagnosis system. Aimed at the representative diagnosis of the hydraulic pressure control system, the three layers feedback network is adopted, the basic theory of conjugate gradient BP neural network is explained in detail, and the key techniques are introduced. Five types of typical faults of hydraulic pressure control system can be distinguished easily by it, the faults diagnosis efficiency is higher 30% than ever and the fault diagnosis capability is better 80% than before.

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Section: Introductionmentioning

confidence: 99%