[1]张米娜,韩红桂,乔俊飞.前馈神经网络结构动态增长-修剪方法[J].智能系统学报,2011,6(02):101-106.
 ZHANG Mina,HAN Honggui,QIAO Junfei.Research on dynamic feedforward neural network structure based on growing and pruning methods[J].CAAI Transactions on Intelligent Systems,2011,6(02):101-106.
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前馈神经网络结构动态增长-修剪方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第6卷
期数:
2011年02期
页码:
101-106
栏目:
出版日期:
2011-04-25

文章信息/Info

Title:
Research on dynamic feedforward neural network structure based on growing and pruning methods
文章编号:
1673-4785(2011)02-0101-06
作者:
张米娜 韩红桂 乔俊飞
北京工业大学 电子信息与控制工程学院,北京 100124
Author(s):
ZHANG Mi’na HAN Honggui QIAO Junfei 
College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
关键词:
自适应增长修剪算法BOD软测量神经网络自组织
Keywords:
adaptive growing and pruning (AGP) BOD softmeasurement neural network self organization
分类号:
TP183
文献标志码:
A
摘要:
针对前馈神经网络隐含层神经元不能在线调整的问题,提出了一种自适应增长修剪算法(AGP),利用增长和修剪相结合对神经网络隐含层神经元进行调整,实现神经网络结构的自组织,从而提高神经网络的性能.同时,将该算法应用于污水处理生化需氧量(BOD)软测量,仿真实验结果表明,与其他自组织神经网络相比,AGP具有较好的泛化能力及较高的拟合精度,能够实现出水BOD的预测.
Abstract:
Due to the unchangable online problem of hidden neurons in feedforward neural networks, an adaptive growing and pruning algorithm (AGP) was presented in this paper. This algorithm can insert and prune hidden neurons during the training process to adjust the structure of the network and achieve self organization of neural network structure, which can improve the performance of the neural network. Additionally, this algorithm has been applied to the biochemical oxygen demand (BOD) soft measurement of the wastewater treatment process. Experimental results show that the proposed algorithm can forecast the effluent BOD with better generalization ability and higher accuracy than other selforganizing neural networks.

参考文献/References:

[1]乔俊飞, 张颖. 一种多层前馈神经网络的快速修剪算法[J]. 智能系统学报, 2008, 3(2): 173176.
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备注/Memo

备注/Memo:
收稿日期:2010-04-22.
基金项目:国家“863” 计划资助项目(2007AA04Z160);国家自然科学基金资助项目(60873043);北京市自然科学基金资助项目(4092010); 高等学校博士点专项科研基金资助项目(200800050004) .
通信作者:张米娜.
E-mail:zhang.mi.na@163.com.
作者简介:
张米娜,女,1986年生,硕士研究生,主要研究方向为神经网络结构优化设计、智能控制理论与应用.
韩红桂,男,1983年生,博士研究生,主要研究方向为智能信息处理、智能控制理论与应用.
乔俊飞,男,1968年生,教授,博士,主要研究方向为神经网络结构分析与设计、计算智能与智能优化控制.
更新日期/Last Update: 2011-05-19