Research on gas concentration identification based on sparrow search algorithm optimization SVR

To address the challenge of quantitatively identifying mixed gases, we developed a gas concentration identification algorithm based on the sparrow search algorithm (SSA) and optimized support vector regression (SVR). The Tent chaotic mapping operator is employed to initialize the population, enhanci...

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Veröffentlicht in:International journal on smart sensing and intelligent systems Jg. 18; H. 1
Hauptverfasser: Zhang, Yuanman, Zou, Yanan, Wu, Qingyun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Sydney Sciendo 01.01.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
electronic nose
gas concentration prediction
gas sensor array
Genetic algorithms
Optimization
Search algorithms
sparrow search algorithm
Support vector machines
support vector regression
ISSN:1178-5608, 1178-5608
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Zusammenfassung:To address the challenge of quantitatively identifying mixed gases, we developed a gas concentration identification algorithm based on the sparrow search algorithm (SSA) and optimized support vector regression (SVR). The Tent chaotic mapping operator is employed to initialize the population, enhancing population diversity, and improving the algorithm’s global search capability. By optimizing SVR parameters with SSA, we propose an enhanced TSSA-SVR model. Evaluated on mixed gas datasets, TSSA-SVR achieves a prediction accuracy of 94.47%, outperforming comparative algorithms such as Genetic Algorithm (GA)-SVR and PSO-SVR, while demonstrating improved convergence compared to the baseline SSA-SVR. The experimental results demonstrate significant performance enhancements, offering an effective solution for precise gas concentration identification in complex environments.
Bibliographie:ObjectType-Article-1
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ISSN:1178-5608
1178-5608
DOI:10.2478/ijssis-2025-0038