Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.
- DOI
- 10.1016/j.neunet.2015.04.007
- Published
- 2015 Oct
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2015.04.007,
title = {Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.},
author = {Vaughan A and Bohac SV},
year = {2015},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2015.04.007},
url = {https://doi.org/10.1016/j.neunet.2015.04.007}
}RIS
TY - JOUR TI - Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series. AU - Vaughan A AU - Bohac SV PY - 2015 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2015.04.007 UR - https://doi.org/10.1016/j.neunet.2015.04.007 ER -
APA
A, V., & SV, B. (2015). Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2015.04.007
Source records
- pubmed · retrieved 2026-09-26T00:33:27.151Z