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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (12): 49-62    DOI: 10.11925/infotech.2096-3467.2017.0786
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Construction of Aggregation Quality Predicting Model for Digital Resource in Library ——Based on Improved Genetic Algorithm and BP Neural Network
Yan Jing1,2(), Bi Qiang1, Li Jie1, Wang Fu1
1School of Management, Jilin University, Changchun 130022, China
2School of Economic Management, Northeast Electric Power University, Jilin 132012, China
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Abstract  

[Objective] This paper proposes a model to predict the quality of library digital resource aggregation with the help of improved BP neural network based on genetic algorithm. [Methods] The genetic algorithm is simple in computing, less dependent on the problems to be solved, and could quickly calculate concurrent threads. First, we obtained the initial weight and threshold with increased population diversity,selection, crossover and variation. Second, we introduced the improved genetic algorithm to the BP neural network, which rapidly reached the fitness setting level by constantly adjusting the weight and threshold values. Finally, we further optimized the performance of the prediction model. [Results] We used MATLAB R2014a platform to examine the proposed model and the average number of prediction errors was 2.74E-04, which was smaller than the actual data. It took the program 18.56 seconds or three steps to finish the task. The prediction accuracy and efficiency of the proposed model was better than the single genetic or BP algorithms. [Limitations] The quality of sample data needs to be improved. We did not compare our training time and prediction accuracy with those of other quick training functions. The population numbers are limited due to computational complexity. [Conclusions] The proposed model could predict the quality of digital resource aggregation efficiently and objectively.
Key words： Digital Resource      Aggregation Quality      Model Construction      Genetic Algorithm      BP Neural Network     
Received: 08 August 2017      Published: 29 December 2017
ZTFLH:  G25 TP393  
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Cite this article:

Yan Jing,Bi Qiang,Li Jie,Wang Fu. Construction of Aggregation Quality Predicting Model for Digital Resource in Library ——Based on Improved Genetic Algorithm and BP Neural Network. Data Analysis and Knowledge Discovery, 2017, 1(12): 49-62.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0786     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I12/49

来源 数字图书馆 网址
国外 1.Library of Congress https://www.loc.gov
2.Elsevier Library http://www.sciencedirect.com
3.EmeraldPublish http://emeraldgrouppublishing.com
4.Cambridge University
Library		 http://www.lib.cam.ac.uk
5.California State Library http://www.library.ca.gov
国内 6.国家图书馆 http://www.nlc.cn
7.知网 http://www.cnki.net
8.万方 http://www.wanfangdata.com.cn
9.吉林大学图书馆 http://lib.jlu.edu.cn
10.广州图书馆 http://www.gzlib.gov.cn
编号 X3 X4 X13 X16 X20 X21 X22
1 30 54 100.00% 5.96% 0.003 29 4.74
2 43 42 100.00% 5.63% 0.002 31 2.72
3 38 39 100.00% 4.35% 0.002 8 4.16
4 39 38 80.00% 3.98% 0.002 48 4.08
5 47 40 100.00% 2.61% 0.002 42 4.15
6 49 60 100.00% 7.68% 0.001 54 4.98
7 30 51 100.00% 6.72% 0.001 18 4.24
8 81 30 100.00% 6.53% 0.003 20 4.03
9 30 31 66.67% 3.62% 0.003 19 3.45
10 74 34 100.00% 6.68% 0.001 17 3.37
说明 以10个文献
为样本估计		 以10个文献
为样本估计		 结合数字图书馆官方
数据和用户自主发布
资源抽样估计		 Post随机检索词10个为样本估计 以链接关系为边,
以资源站为节点		 度大于10的核心
子网节点数量		 待测评数字
图书馆节点
接近中心度
编号 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 输出
1 2.61 2.61 2.61 3.48 2.61 1.74 2.61 2.61 1.74 2.61 2.61 3.48 3.48 3.48
2 2.63 2.63 2.63 2.63 3.51 2.63 1.75 2.63 2.63 3.51 1.75 0.88 3.51 2.63
3 2.59 3.45 2.59 2.59 3.45 2.59 3.45 3.45 2.59 2.59 2.59 2.59 3.45 2.59
4 2.56 2.56 2.56 2.56 2.56 2.56 2.56 1.71 2.56 3.42 2.56 2.56 2.56 3.42
5 2.64 2.64 2.64 2.64 3.52 2.64 1.76 2.64 2.64 3.52 2.64 2.64 2.64 2.64
6 3.49 3.49 2.62 3.49 2.62 2.62 3.49 3.49 3.49 2.62 2.62 3.49 3.49 3.49
7 4.35 3.48 2.61 3.48 3.48 2.61 4.35 3.48 2.61 3.48 3.48 3.48 3.48 4.35
8 4.35 2.61 3.48 2.61 3.48 3.48 3.48 2.61 2.61 3.48 3.48 2.61 3.48 3.48
9 3.48 2.61 2.61 2.61 3.48 2.61 1.74 2.61 2.61 3.48 2.61 2.61 1.74 2.61
10 2.61 2.61 3.48 2.61 2.61 3.48 2.61 3.48 2.61 2.61 3.48 2.61 3.48 2.61
编号 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 输出
1 1.74 3.48 3.48 3.48 3.48 3.48 3.48 2.61 3.48 3.48 2.61 3.48 2.61 3.48
2 1.75 3.51 2.63 2.63 0.88 1.75 2.63 2.63 1.75 3.51 1.75 1.75 1.75 2.63
3 2.59 2.59 2.59 3.45 2.59 3.45 2.59 2.59 3.45 3.45 3.45 2.59 3.45 2.59
4 2.56 3.42 2.56 2.56 3.42 2.56 3.42 3.42 2.56 2.56 3.42 2.56 3.42 3.42
5 2.64 2.64 1.76 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64
6 3.49 3.49 3.49 3.49 3.49 2.62 2.62 3.49 3.49 2.62 3.49 2.62 3.49 3.49
7 3.48 2.61 3.48 3.48 3.48 2.61 2.61 2.61 3.48 3.48 3.48 3.48 2.61 4.35
8 3.48 2.61 3.48 3.48 2.61 2.61 3.48 2.61 2.61 3.48 2.61 3.48 2.61 3.48
9 2.61 2.61 2.61 2.61 2.61 2.61 3.48 2.61 2.61 3.48 2.61 3.48 2.61 2.61
10 2.61 2.61 3.48 2.61 2.61 2.61 1.74 2.61 2.61 2.61 3.48 2.61 2.61 2.61
输入参数 输入值 输入参数 输入值
输入层节点数 26 学习效率 0.9
隐含层节点数 17 动态参数 0.7
输出层节点数 1 允许误差 0.00001
进化代数 80 交叉概率 0.3
迭代次数 100 变异、选择概率 0.1
样本
编号		 实际
值		 预测
值		 误差 样本
编号		 实际
值		 预测
值		 误差
1 3.48 3.4631 -1.31E-03 5 2.64 2.6154 2.42E-04
2 2.63 2.6637 5.12E-04 6 3.49 3.4453 0.91E-03
3 2.59 2.5165 3.47E-04 7 4.35 4.3212 -2.13E-04
4 3.42 3.4132 6.25E-04 8 3.48 3.4452 -1.43E-03
模型 样本编号 实际值 预测值 误差 平均误差
GA2BP 9 2.61 2.6076 3.12E-04 2.74E-04
10 2.61 2.6077 2.35E-04
GA 9 2.61 2.6215 1.15E-02 1.14E-02
10 2.61 2.6212 1.12E-02
BP 9 2.61 2.6154 5.43E-03 4.57E-03
10 2.61 2.6137 3.70E-03
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