All deep‐groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high‐order and coupled‐variable function. This paper has verified that the stiffness function for all deep‐groove ball bearings can be replaced by a back‐propagation neural network (BPNN) which is trained by using some (not all) samples.
Kang, Y., Shen, P., Chen, C., Huang, C. and Yang, L. (2004), "A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings", Industrial Lubrication and Tribology, Vol. 56 No. 3, pp. 147-157. https://doi.org/10.1108/00368790410532183Download as .RIS
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