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Ge, Z. and Song, Z. (2010) A Comparative Study of Just-in-Time-Learning Based Methods for Online Soft Sensor Modeling. Chemometrics and Intelligent Laboratory Systems, 104, 306-317.
https://doi.org/10.1016/j.chemolab.2010.09.008

has been cited by the following article:

• TITLE: A New Efficient Obstacle Avoidance Control Method for Cars Based on Big Data and Just-in-Time Modeling

  AUTHORS: Tatsuya Kai

  KEYWORDS: Big Data, Just-in-Time Modeling, Cars, Obstacle Avoidance Control

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 13, 2018

  ABSTRACT: This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.