Machine Learning: A Crucial Tool for Sensor Design

Algorithms

Weixiang Zhao¹  Abhinav Bhushan¹  Anthony D. Santamaria¹  Melinda G. Simon¹ 

[1] id="af1-algorithms-01-00130">Department of Mechanical and Aeronautical Engineering, One Shields Avenue, University of California, Davis, CA 95616, U

关键词: Sensors;    Machine Learning;    Feature Extraction;    Multivariate Analysis;    Neural Networks;    Optimization;   

DOI  :  10.3390/a1020130

来源: mdpi

【 摘 要 】

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

【 授权许可】

CC BY   
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.