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| 基于优化的深度极限学习机的柴油车NOx排放预测 |
| Nox Emission Prediction of Diesel Vehicles Based on Optimized Deep Extreme Learning Machine |
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| DOI： |
| 中文关键词: NOx 重型柴油车 麻雀搜索算法 深度极限学习机 排放预测 |
| 英文关键词: NOx Heavy-duty diesel vehicles Sparrow search algorithm DELM Emissions prediction |
| 基金项目:上海市科技计划“上海大气颗粒物和臭氧精细化监测预报及关键前提污染物防控关键技术研究与应用示范”基金资助项目（20dz1204000）；上海市2022年度“科技创新行动计划”科技支撑碳达峰碳中和专项“城市货运交通碳排放实时计算与监测关键技术研究与示范”基金资助项目（22dz1207506） |
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| 中文摘要: |
| 用麻雀搜索算法优化的深度极限学习机（SSA-DELM）构建柴油车NOx排放预测模型，对柴油车低速、中速和高速状态下的NOx排放进行预测，并将此模型性能与深度极限学习机（DELM）模型性能进行对比分析。结果表明：SSA-DELM模型的预测效果较好，在低速、中速、高速状态下该模型平均绝对百分比误差MAPE分别为0.061 0、0.044 9、0.039 1；在低速、中速、高速状态下SSA-DELM模型的性能评价指标比DELM模型性能评价指标分别优约23%、44%、11%。 |
| 英文摘要: |
| A deep extreme learning machine（SSA-DELM）optimized by Sparrow search algorithm was used to construct a NOx emission prediction model for diesel vehicles to predict NOx emission of diesel vehicles at low， medium and high speeds. The performance of this model was compared with that of deep extreme learning machine （DELM） model. The results showed that SSA-DELM model had good prediction effect， with a mean absolute percentage error MAPE of 0.061 0， 0.044 9 and 0.039 1 at low， medium and high speeds， respectively. The performance evaluation indexes of SSA-DELM model were about 23%， 44% and 11% better than those of DELM model at low， medium and high speeds， respectively. |
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