优化增量型随机权神经网络及应用

姜乐; 周平

doi:10.11949/0438-1157.20190635

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优化增量型随机权神经网络及应用

过程系统工程 | 更新时间：2026-07-24

- 优化增量型随机权神经网络及应用
- Optimized incremental random vector functional-link networks and its application
- 化工学报 2019年70卷第12期页码：4710-4721
- 作者机构：
  
  东北大学流程工业综合自动化国家重点实验室，辽宁沈阳 110819
- 作者简介：
  
  姜乐(1994—)，女，硕士研究生，1649577095@qq.com
  周平(1980—)，男，博士，教授，博士生导师，zhouping@mail.neu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61890934;61473064;61790572;61890930);中央高校基本科研业务费专项资金(N180802003);矿冶过程自动控制技术国家（北京市）重点实验室开放课题(BGRIMM-KZSKL-2017-04)
- DOI：10.11949/0438-1157.20190635
  中图分类号： TQ 028.8
- 收稿：2019-06-10，
  
  修回：2019-08-12，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
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姜乐, 周平. 优化增量型随机权神经网络及应用[J]. 化工学报, 2019, 70(12): 4710-4721

JIANG Yue, ZHOU Ping. Optimized incremental random vector functional-link networks and its application[J]. CIESC Journal, 2019, 70(12): 4710-4721
姜乐, 周平. 优化增量型随机权神经网络及应用[J]. 化工学报, 2019, 70(12): 4710-4721 DOI： 10.11949/0438-1157.20190635.

JIANG Yue, ZHOU Ping. Optimized incremental random vector functional-link networks and its application[J]. CIESC Journal, 2019, 70(12): 4710-4721 DOI： 10.11949/0438-1157.20190635.

摘要

针对传统增量型随机权神经网络(I-RVFLNs)存在网络参数难以优化确定、模型收敛速度慢和结构复杂的问题，提出一种优化增量型随机权神经网络算法，即O-I-RVFLNs。与传统I-RVFLNs不同，所提O-I-RVFLNs算法首先设定了一个期望的建模残差向量，然后在每次新增隐层节点时，选择可以达到或小于此节点期望残差的输入权值和偏置作为该节点的输入参数，进而提高网络的收敛速度。除此之外，考虑到算法在不断迭代更新过程中建模误差越来越小，下降趋势越来越不明显的问题，将各指标参数相邻两次迭代均方根误差的差值考虑在算法终止条件内，并借鉴统计过程控制中的西电规则制定了相应的算法收敛判定准则。最后，基于UCI能效数据和实际高炉工业数据，对所提O-I-RVFLNs算法进行了验证和应用。结果表明，相对于其他RVFLNs算法，所提算法建立的数据模型能够获得更紧凑的网络结构以及更好的泛化性能和预测精度。

Abstract

Aiming at the problem that network parameters are difficult to be optimally determined

the model convergence speed is slow and the structure is complex in the traditional incremental random vector functional-link networks (I-RVFLNs)

an optimized incremental random vector functional-link networks algorithm

namely O-I-RVFLNs

is proposed. Different from the traditional I-RVFLNs

the proposed O-I-RVFLNs algorithm sets a desired residual error vector

and then selects the input weights and biases that can reach or less than the expected residual error as the input parameters of the node each time a hiddennode is added

thereby improving the convergence rate of the network. In addition

considering that the modeling error of the algorithm is smaller and smaller and the downward trend is less obvious in the process of continuous iteration updating

the RMSE difference between adjacent iterations of each index parameter is considered in the termination condition of the algorithm

and the corresponding convergence criteria are formulated by referring to the Western Electricity Rules in statistical process control. Finally

based on UCI energy efficiency data and actual blast furnace industrial data

the proposed O-I-RVFLNs algorithm is verified and applied. The results show that compared with other RVFLNs algorithms

the data model built by the proposed algorithm can obtain more compact network structure

better generalization performance and prediction accuracy.

关键词

Keywords

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