首页|基于麻雀搜索算法优化BP人工神经网络的短期湍流预报模型研究

基于麻雀搜索算法优化BP人工神经网络的短期湍流预报模型研究

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提出了一种基于麻雀搜索算法优化BP人工神经网络(SSA-BP)的湍流预报模式.首先,采用BP人工神经网络作为湍流预报模型的基础框架.通过对温度、湿度、风速等气象因素的采集和处理,将其作为输入层的特征.然后,利用麻雀搜索算法对BP人工神经网络的权重和偏置进行优化.为了验证该方法的有效性,采用了来自地面气象站的大气湍流数据及气象数据进行实验.实验结果表明,SSA-BP人工神经网络能够成功预测大气湍流的发展趋势,并具有较高的预测精度和稳定性,能够充分利用大气湍流数据中的非线性特征,为湍流预测研究和实际应用提供了有力支持.
Research on Short-term Turbulence Forecasting Model Based on Sparrow Search Algorithm to Optimize BP Artificial Neural Network
A turbulence prediction model based on sparrow search algorithm optimized BP artificial neural network(SSA-BP) has been proposed. Firstly ,the BP artificial neural network is adopted as the basic framework for turbulence prediction models. By collecting and processing meteorological factors such as temperature ,humidity ,and wind speed ,they are used as input layer features. Then ,the sparrow search algorithm is used to optimize the weights and biases of the BP artificial neu-ral network. To verify the effectiveness of this method ,experiments were conducted using atmospheric turbulence data and meteorological data from ground meteorological stations. The experimental results indicate that the SSA-BP artificial neural network can successfully predict the development trend of atmospheric turbulence ,and has high prediction accuracy and sta-bility. Being able to fully utilize the nonlinear features in atmospheric turbulence data provides strong support for turbulence prediction research and practical applications.

BP artificial neural networksparrow search algorithmseteorological parametersatmospheric turbulence prediction

张恒、张雷、姚海峰、佟首峰、曹玉玺

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长春理工大学 光电工程学院,长春 130022

北京理工大学 光电学院,北京 100081

BP人工神经网络 麻雀搜索算法 气象参数 大气湍流预测

国家自然科学基金中国博士后科学基金特别资助项目中国博士后科学基金面上项目应用光学国家重点实验室项目吉林省教育厅科学技术研究项目

621050292021TQ00352021M700415SKLA02022001A11JJKH20220743KJ

2024

长春理工大学学报(自然科学版)
长春理工大学

长春理工大学学报(自然科学版)

CSTPCD
影响因子：0.432
ISSN：1672-9870
年,卷(期)：2024.47(2)
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