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Hengyu Cao

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Proceedings Article | 2 March 2022 Paper

Combinative improvement of adaptable prediction using half-model based methods

Hengyu Cao

Proceedings Volume 12158, 1215815 (2022) https://doi.org/10.1117/12.2626925

KEYWORDS: Data modeling, Neural networks, Model-based design, Performance modeling, Sensors, MATLAB, Environmental sensing

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This paper covers adjustment and improvement of optimized adaptable prediction using non-model based methods and neural network. The project has combined the advantages of conventional model-based adaptable prediction and the updated adaptable prediction model based on a trained neural network. While the updated adaptable prediction model can increase the prediction performance in large data slope condition, the conventional adaptable prediction model can also correct the prediction error when slope of data is small. All of the obtained results will be analyzed and compared with model-based results. Limitations of each model will also be described in the context. This paper proposes a half-model based prediction for vehicle interactions in unstructured environments.

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