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Conference Papers Year : 2020

A new neural network feature importance method: Application to mobile robots controllers gain tuning

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Ashley Hill
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  • PersonId : 1107005
Eric Lucet

Abstract

This paper proposes a new approach for feature importance of neural networks and subsequently a methodology to determine useful sensor information in high performance controllers, using a trained neural network that predicts the quasi-optimal gain in real time. The neural network is trained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to lower a given objective function. The important sensor information for robotic control are determined using the described methodology. Then a proposed improvement to the tested control law is given, and compared with the neural network's gain prediction method for real time gain tuning. As a results, crucial information about the importance of a given sensory information for robotic control is determined, and shown to improve the performance of existing controllers.
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Dates and versions

cea-03314585 , version 1 (05-08-2021)

Identifiers

  • HAL Id : cea-03314585 , version 1

Cite

Ashley Hill, Eric Lucet, Roland Lenain. A new neural network feature importance method: Application to mobile robots controllers gain tuning. ICINCO 2020, 17th International Conference on Informatics in Control, Automation and Robotics, Jul 2020, Paris, France. ⟨cea-03314585⟩
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