By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ...
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By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
民机座舱热舒适性研究对我国研制的大型飞机市场竞争力具有重要意义.在舒适性研究中人体热模型的合理性是影响舒适性分析与评价的关键.在开展的有限空间人员热特性实验测试基础上,建立了一种具有热调节行为的人体热模型,将人体复杂传热过程抽象为高温核心区、血液灌注区和等效组织区三者间热传递,很好地克服了常规定壁温或定热流密度人体模型的局限性,计算结果与实验结果吻合较好.将该模型结合文中提出的无迭代PMV(Predicted Mean Vote)热舒适性方程,在保证计算速度的前提下,可实现高人员密度的民机座舱舒适性分析.仿真结果表明:该方法既能够满足复杂空间的计算速度要求,又能较准确地体现人体生理热调节过程,仿真结果可靠性高.
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