Improved boosting and self-attention RBF networks for COD prediction based on UV-vis-Reference-Cited by-同舟云学术

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Improved boosting and self-attention RBF networks for COD prediction based on UV-vis

Published:2024 Issue: Volume: Page:
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Chen Xi'ang¹²³^ORCID,Wang Senlin¹⁴³,Chen Hao¹⁴³,Fan Renhao¹²³^ORCID

Affiliation:

1. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350005, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362200, China

4. Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou 362200, China

Abstract

A self-attention mechanism is introduced into the radial basis function (RBF) network, resulting in a COD detection model based on the saRBF framework.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/AY/D4AY01441C

Reference32 articles.

1. Chemical oxygen demand: Historical perspectives and future challenges

2. Analytical Approaches for Determining Chemical Oxygen Demand in Water Bodies: A Review

3. Mechanisms of potassium permanganate pretreatment improving anaerobic fermentation performance of waste activated sludge

4. Chlorate induced false reduction in chemical oxygen demand (COD) based on standard dichromate method: Countermeasure and mechanism

5. Determination of coagulant dosages for process control using online UV-vis spectra of raw water

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