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数据分析与知识发现  2017, Vol. 1 Issue (12): 49-62     https://doi.org/10.11925/infotech.2096-3467.2017.0786
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
图书馆数字资源聚合质量预测模型构建*——基于改进遗传算法和BP神经网络
闫晶1,2(), 毕强1, 李洁1, 王福1
1吉林大学管理学院 长春 130022
2东北电力大学经济管理学院 吉林 132012
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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摘要 

目的】针对图书馆数字资源聚合质量评价要求, 基于遗传算法对BP神经网络进行改进, 进而构建更为优化的图书馆数字资源聚合质量预测模型。【方法】利用遗传算法计算简单、对待求解问题依赖小、并发线程计算速度快等优点, 通过广义海明距离定义种群提高种群多样性, 进行种群选择、交叉、变异操作, 求解初始权重和阈值; 将改进的遗传算法引入BP神经网络, 通过权重和阈值的不断调整, 快速收敛至适应度设定值, 最终实现预测结果的进一步优化。【结果】采用MATLAB R2014a平台进行仿真实验, 预测结果平均误差2.74E-04, 同实际数据误差小, 模型精度较高。程序运行总时长18.56秒, 且三步就收敛到误差目标, 模型收敛速度快, 相较单一的遗传算法和BP算法具有更高的预测精度和效率。【局限】样本数据质量有待提高; 实验中未采用Train的其他快速训练函数进行训练时间和预测精度对比; 种群数量因计算复杂性而受限。【结论】模型能够对图书馆数字资源聚合质量做出高效、客观预测, 应用前景和延展性较好, 能有效运用于图书馆数字资源聚合质量评价结果检验、大样本评价以及大样本预测领域。

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闫晶
毕强
李洁
王福
关键词 数字资源聚合质量模型构建遗传算法BP神经网络    
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 wordsDigital Resource    Aggregation Quality    Model Construction    Genetic Algorithm    BP Neural Network
收稿日期: 2017-08-08      出版日期: 2017-12-29
ZTFLH:  G25 TP393  
基金资助:*本文系国家自然科学基金项目“语义网络环境下数字资源多维度聚合与可视化研究”(项目编号: 71273111)的研究成果之一
引用本文:   
闫晶, 毕强, 李洁, 王福. 图书馆数字资源聚合质量预测模型构建*——基于改进遗传算法和BP神经网络[J]. 数据分析与知识发现, 2017, 1(12): 49-62.
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.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.0786      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I12/49
  BP神经网络模型
  遗传算法步骤
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
  国内外数字图书馆样本
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
  客观指标基础数据表
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
  样本数据表
  GA2BP图书馆数字资源聚合质量预测模型
  GA2BP模型算法框架
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
  模型参数输入数据
  模型训练过程
样本
编号
实际
预测
误差 样本
编号
实际
预测
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
  GA2BP模型训练样本输出
  GA2BP模型训练误差
  GA2BP模型训练迭代次数
  误差进化曲线
  训练性能曲线
(注: Best与Goal重合。)
  训练状态曲线
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
  三种不同模型检测样本输出结果对比
  GA2BP同GA、BP预测结果对比
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