鲁棒加权最小二乘支持向量回归的进气量预测

杨济东; 南新元; 查琴

doi:10.13568/j.cnki.651094.651316.2021.02.25.0001

您当前的位置：

首页 >

文章列表页 >

鲁棒加权最小二乘支持向量回归的进气量预测

更新时间：2026-01-21

- 鲁棒加权最小二乘支持向量回归的进气量预测
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 39, Issue 2, Pages: 189-196(2022)
- 作者机构：
  
  新疆大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2021.02.25.0001
  CLC： TP181;TF831
- Published：2022
- 稿件说明：
移动端阅览
[1]杨济东,南新元,查琴.鲁棒加权最小二乘支持向量回归的进气量预测[J].新疆大学学报(自然科学版)(中英文),2022,39(02):189-196.
[1]杨济东,南新元,查琴.鲁棒加权最小二乘支持向量回归的进气量预测[J].新疆大学学报(自然科学版)(中英文),2022,39(02):189-196. DOI： 10.13568/j.cnki.651094.651316.2021.02.25.0001.

DOI：10.13568/j.cnki.651094.651316.2021.02.25.0001.

摘要

生物氧化提金过程中，进气量是决定提金率和生产成本的重要参数．针对进气量受众多因素影响且具有一定随机性的问题，提出了一种基于鲁棒加权最小二乘支持向量回归的进气量预测模型．通过分析得知进气量的局部波动数据与氧化槽的氧化还原电位之间存在较大的相关性，考虑实际现场情况，根据相关性程度赋予数据不同权重，将两组特定权重的交集应用于加权最小二乘支持向量回归算法，建立了鲁棒加权最小二乘支持向量回归进气量预测模型．仿真结果表明，所建模型可行有效．

Abstract

In the process of biooxidation gold extraction by bioleaching bacteria

air intake is an important parameter. It has a direct impact on the final gold extraction rate and production cost. For the randomness of the intake volume

which is affected by many factors

a robust weighted least squares support vector regression for air input predictive control is presented. By analyzing

it is known that there is a great correlation between the local abnormal data of air intake and the redox potential of oxidation tank. According to the actual production situation

a weighting method is proposed to give different weights to data on the basis of the degree of correlation.These weights are obtained by mixing two specific sets

which is applied to the weighted least squares support vector regression algorithm. So a robust weighted least squares support vector regression model is presented for air intake prediction. Finally

simulation results show that the proposed model is feasible and effective.

关键词

Keywords

references

QIN T, ZENG S, GUO J. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model[J]. Microelectronics Reliability, 2015, 55(9):1280-1284.

南新元,曾庆凯,孟宪强.基于改进蛙跳算法优化的支持向量回归机ORP预测[J].新疆大学学报(自然科学版), 2016, 33(4):393-398.

TASHTARIAN F, YAGHMA H, SOHRABY K, et al. On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks[J]. IEEE Transactions on Vehicular Technology, 2015, 64(7):3177-3189.

JUN S, XIN Z, XIAOHONG W, et al. Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy, 2019, 212:215-221.

CHEN X, YANG J, LIANG J, et al. Recursive robust least squares support vector regression based on maximum correntropy criterion[J]. Neurocomputing, 2012, 97(8):63-73.

BEHNASR M, JAZAYERI H. Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm[J]. Journal of Natural Gas Science&Engineering, 2015, 22(11):35-41.

IORKYASE E T, TACHTATZIS C, LAZARIDIS P, et al. Radio location of partial discharge sources:a support vector regression approach[J]. Iet Science Measurement&Technology, 2018, 12(2):230-236.

SHAHSAVAR A S, BAGHERZADEH S A, MAHMOUDI B, et al. Robust weighted least squares support vector regression algorithm to estimate the nanofluid thermal properties of water/graphene oxidesilicon carbide mixture[J]. Physica A:Statistical Mechanics and its Applications, 2019, 525(10):1418-1428.

刘京礼,李建平,徐伟宣,等.信用评估中的鲁棒赋权自适应最小二乘支持向量机方法[J].中国管理科学, 2010, 18(5):28-33.

顾燕萍,赵文杰,吴占松.最小二乘支持向量机鲁棒回归算法研究[J].清华大学学报(自然科学版), 2015, 55(4):396-402.

SANTOS A, DANIEL J, BARRETO G A. Novel sparse LSSVR models in primal weight space for robust system identification with outliers[J]. Journal of Process Control, 2017, 67(4):26-35.

GE Z, SONG Z. A comparative study of just-in-time-learning based methods for online soft sensor modeling[J]. Chemometrics&Intelligent Laboratory Systems, 2010, 104(2):306-317.

SUYKENS J A K, BRABANTER J D, LUKAS L, et al. Weighted least squares support vector machines:robustness and sparse approximation[J]. Neurocomputing, 2002, 48(1):85-105.

WALLEY P. Statistical reasoning with imprecise probabilities[M]. London:Chapman and Hall, 1991.

UTKIN L V, COOLEN F P A. A robust weighted SVR-based software reliability growth model[J]. Reliability Engineering&System Safety, 2018, 176(8):93-101.

蔡鑫,南新元,高丙朋,等.生物氧化预处理过程中进气量预测智能集成模型的建立[J].湿法冶金, 2016, 35(4):288-292.

Views

318

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰