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Download fileLocal linear model tree with optimized structure
journal contribution
posted on 2021-04-29, 15:49 authored by Xiaoyan Hu, Yu GongYu Gong, Dezong Zhao, Wen GuThis paper investigates the local linear model tree (LOLIMOT) with optimized structure. The performance of the LOLIMOT model depends on how the neurons are constructed. In the typical LOLIMOT model, the number of neurons is initially set as one and starts to increase by repeatedly splitting an existing neuron into two equal ones until the required performance is achieved. Because the equal split of a neuron is not optimal, a large model size is often necessary for required performance, leading to high complexity and strong overfitting. In this paper, we propose a gradient-decent-search-based algorithm to optimally split an existing neuron into two new ones. Based on both numerical data and simulated engine data, through the evaluation of optimized structure, the effectiveness of proposed method has been verified.
Funding
Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Engineering and Physical Sciences Research Council
Find out more...History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
- Mechanical, Electrical and Manufacturing Engineering
Department
- Aeronautical and Automotive Engineering
Published in
IFAC-PapersOnLineVolume
53Issue
2Pages
1163 - 1168Publisher
Elsevier BVVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2021-04-14Copyright date
2020ISSN
2405-8963Publisher version
Language
- en