加速度计
梯度下降
人工神经网络
校准
加速度
非线性系统
控制理论(社会学)
重力加速度
计算机科学
职位(财务)
力矩(物理)
最速下降法
算法
数学
数学优化
人工智能
万有引力
统计
物理
操作系统
经典力学
经济
量子力学
财务
控制(管理)
作者
Mario A. Soriano,Faheem Ahmed Khan,Rafiq Ahmad
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:69 (9): 6787-6794
被引量:21
标识
DOI:10.1109/tim.2020.2978568
摘要
Currently, there is no robust method that could calibrate the accelerometer output without explicitly deriving the error model of the device and estimate the nonlinear parameters of the model. This article presents a methodology to approximate the output of two-axis thermal accelerometers based on neural networks (NNs) for calibration and nonlinear correction. This method uses the output of the accelerometer and the Earth's gravitational acceleration expected at a static position as data for training. The proposed method uses different optimization methods (adaptive moment estimation (ADAM), gradient descent, and gradient descent with momentum) to find the best solution using half mean squared error (HMSE) as the cost functions for evaluation. Experiments are conducted and presented to validate the NN-based calibration method using 2800 unseen data points.
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