doi:10.16186/j.cnki.1673-9787.2020.5.17

Received:2020/01/09

Revised:2020/03/10

Published:2020/09/15

Iterative algorithm for redundancy value of hybrid cloud data based onlearning rate and update vector

ZHANG Xiaoli1,2

1.School of Mechanical Engineering and Automation， Northeastern University， Shenyang  110819 ， Liaoning， China;2.School of Mathematics， Changchun Normal University， Changchun  130032 ， Jilin， China

Abstract:Aiming at the problems of low average return value， poor convergence stability and small scale of convergence action for the traditional hybrid cloud data redundancy value iterative algorithm， a hybrid cloud data redundancy value iterative algorithm based on learning rate and update vector was proposed. Firstly， a hybrid cloud data redundancy value function was constructed， a new parameter was introduced into the function to update the weight vector， and a stable value of the value function was acquired based on learning rate requirements of deep learning ； Secondly， the value function was calculated according to the obtained stable value， the stable value vector was used to process the stable value vector with new weights， and the value function update vector was obtained； Finally， the weight increment was calculated， the iterative study of the redundant value of hybrid cloud data was completed by combining the Hash table with the calculation. The experimental results showed that the average return value of the algorithm was the highest and the convergence speed was the fastest.

Key words:learning rate;update vector;deep learning;hybrid cloud data;redundancy value iteration algorithm

基于学习速率与更新向量的混合云数据冗余值迭代算法_张晓丽.pdf