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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2019

14.11.2017 | Original Article

Haptic recognition using hierarchical extreme learning machine with local-receptive-field

verfasst von: Fengxue Li, Huaping Liu, Xinying Xu, Fuchun Sun

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2019

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Abstract

In order to perform useful tasks in people’s daily life, robots must be able to both communicate and understand the sensations they experience and may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose an effective hierarchical extreme learning machine with local-receptive-field architecture, while introducing the local receptive field concept in neuroscience and maintaining ELM’s advantages of training efficiency. In this paper, we further extend the LRF-based ELM method to a hierarchical model for haptic classification. Experimental validation on the Penn Haptic Adjective Corpus 2 dataset illustrates that the proposed hierarchical method achieves better recognition performance.
Vorheriger Artikel Adaptive control of nonlinear fractional-order systems using T–S fuzzy method
Nächster Artikel An effective Bayesian network parameters learning algorithm for autonomous mission decision-making under scarce data

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Metadaten
Titel
Haptic recognition using hierarchical extreme learning machine with local-receptive-field
verfasst von
Fengxue Li
Huaping Liu
Xinying Xu
Fuchun Sun
Publikationsdatum
14.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0736-y

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