Abstract : Neural network which can adapt the sample data by training has good fault-tolerance and can be used in the field of intelligence widely. In the embedded system, restricted to the resources and the capacity of processor, the neural network application has a series of problems, such as losing timelines and the system could be collapsed easily. This article discusses how to use limited memory, processor and external equipment resources to achieve the neural network algorithm for improving the universality of detection system and adaptive ability in the embedded intelligent measuring system.
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-347 (Part IV), pp.376-381, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18369-0_43〉
https://hal.inria.fr/hal-01564823
Contributeur : Hal Ifip
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Soumis le : mercredi 19 juillet 2017 - 11:23:23
Dernière modification le : mercredi 19 juillet 2017 - 11:30:17
Xiaodong Liu, Dongzhou Ning, Hubin Deng, Jinhua Wang. The Application Research of Neural Network in Embedded Intelligent Detection. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-347 (Part IV), pp.376-381, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18369-0_43〉. 〈hal-01564823〉
