[1]顾海艳,曹 林,朱 涛,等.神经网络二次集成预测算法研究[J].南京师大学报(自然科学版),2022,45(02):136-141.[doi:10.3969/j.issn.1001-4616.2022.02.017]
　Gu Haiyan,Cao Lin,Zhu Tao,et al.Research on Quadratic Integration Prediction Algorithm of Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(02):136-141.[doi:10.3969/j.issn.1001-4616.2022.02.017]
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神经网络二次集成预测算法研究()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年02期
页码:
136-141
栏目:
·计算机科学与技术·
出版日期:
2022-05-15

文章信息/Info

Title:
Research on Quadratic Integration Prediction Algorithm of Neural Network
文章编号:
1001-4616(2022)02-0136-06
作者:
顾海艳1; 曹 林2; 朱 涛1; 袁 明1
(1.江苏警官学院,江苏 南京 210031)(2.中国人民解放军第66072部队,北京 100144)
Author(s):
Gu Haiyan1; Cao Lin2; Zhu Tao1; Yuan Ming1
(1.Jiangsu Police Institute,Nanjing 210031,China)(2.PLA 66072 Troops,Beijing 100144,China)
关键词:
神经网络二次集成; 量子粒子群算法; 量子免疫算法; 数据预测
Keywords:
two-level neural network ensemble; quantum particle swarm optimization algorithm; quantum immune algorithm; data forecasting
分类号:
TP18
DOI:
10.3969/j.issn.1001-4616.2022.02.017
文献标志码:
A
摘要:
针对常用预测算法不同程度地存在泛化能力不足的缺陷,提出了基于神经网络二次集成的优化算法(NNE2-QQ). 该算法在第一次集成时采用量子粒子群算法进行个体网络的选择优化,在第二次集成时采用量子免疫算法进行集成结论生成优化,并通过多次迭代自适应寻求个体和权值的最佳组合,实现神经网络二次集成模型的性能最优,最后实验验证了NNE2-QQ算法的有效性和实用性. NNE2-QQ可从海量数据中发现各种因素之间的联系及其规律,为预测判断提供支持.
Abstract:
The common prediction algorithms of data mining have the defect of insufficient generalization ability,so this paper proposes an optimization algorithm based on two-level neural network ensemble(NNE2-QQ). The first ensemble uses quantum particle swarm optimization algorithm to select individuals,the second ensemble uses quantum immune algorithm to optimize the result,and then the best ability of the ensemble model is obtained by finding the best selection and weights through multiple iterations. Finally,simulation results demonstrate the practicality and efficiency of NNE2-QQ algorithm. NNE2-QQ can find the relationship between various factors and their laws from massive data,and provide support for prediction and judgment.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金资助项目(61802155)、江苏省教改课题(2019JSJG595).
通讯作者:顾海艳,副教授,研究方向:信息安全、大数据技术和智能优化. E-mail:ghy7388@126.com
更新日期/Last Update: 1900-01-01