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Local linear model tree with optimized structure

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journal contribution
posted on 29.04.2021, 15:49 by Xiaoyan Hu, Yu Gong, Dezong Zhao, Wen Gu
This 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

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Mechanical, Electrical and Manufacturing Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IFAC-PapersOnLine

Volume

53

Issue

2

Pages

1163 - 1168

Publisher

Elsevier BV

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher 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

2021-04-14

Copyright date

2020

ISSN

2405-8963

Language

en

Depositor

Mr Wen Gu. Deposit date: 29 April 2021

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Loughborough Publications

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