论文标题
深度学习传感器性能优化的回归
Regression with Deep Learning for Sensor Performance Optimization
论文作者
论文摘要
至少有两个隐藏层的神经网络称为深网。一般而言，AI和计算机编程的最新发展导致了Tensorflow，Keras，Numpy等工具的开发。使得更容易地对数据进行建模和得出结论。在这项工作中，我们通过Keras和TensorFlow启用了深度学习重新介绍非线性回归。特别是，我们使用深度学习来参数化工业传感器的输入和输出之间的非线性多元关系，以根据所选的密钥指标优化传感器性能。
Neural networks with at least two hidden layers are called deep networks. Recent developments in AI and computer programming in general has led to development of tools such as Tensorflow, Keras, NumPy etc. making it easier to model and draw conclusions from data. In this work we re-approach non-linear regression with deep learning enabled by Keras and Tensorflow. In particular, we use deep learning to parametrize a non-linear multivariate relationship between inputs and outputs of an industrial sensor with an intent to optimize the sensor performance based on selected key metrics.
