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Data-Driven Student Learning Performance Prediction based on RBF Neural Network

Volume 15, Number 6, June 2019, pp. 1560-1569
DOI: 10.23940/ijpe.19.06.p7.15601569

Chunqiao Mia,b

aSchool of Computer Science and Engineering, Huaihua University, Huaihua, 418000, China
bKey Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua, 418000, China

 

(Submitted on March 20, 2019; Revised on April 5, 2019; Accepted on June 7, 2019)

Abstract:

Based on the reduction and redundancy methods, the reliability performance of the improved general series-parallel system is considered, assuming the connected components are identically independent and follow the general exponential lifetime model. To extend previous studies, the shape parameter is modified to obtain the reliability equivalence factors of the hot and cold duplications. A hybrid of the hot and cold duplication methods is also considered. Numerical results from a practical example are investigated to illustrate the derived theoretical results of the overall study.

 

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