Three-Layer Weighted Fuzzy Support Vector Regression for Emotional Intention Understanding in Human-Robot Interaction

A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. The TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means for...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 26; číslo 5; s. 2524 - 2538
Hlavní autoři: Chen, Luefeng, Zhou, Mengtian, Wu, Min, She, Jinhua, Liu, Zhentao, Dong, Fangyan, Hirota, Kaoru
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Back propagation networks
Clustering
Clustering algorithms
Computing time
Emotions
Fuzzy inference
Human-robot interaction
human–robot interaction (HRI)
intention understanding
Kernel
kernel fuzzy c-means (KFCM)
Model accuracy
Neural networks
Regression models
Robots
Support vector machines
support vector regression (SVR)
Training
Training data
ISSN:1063-6706, 1941-0034
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Shrnutí:A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. The TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means for data clustering, fuzzy support vector regressions (FSVR) for information understanding, and weighted fusion for intention understanding. It aims to guarantee the quick convergence and satisfactory performance of the local FSVR via adjusting the weights of each feature in each cluster, in such a way that importance of different emotion-identification information is represented. Moreover, smooth human-oriented interaction can be obtained by endowing robot with human intention understanding capability. Experimental results show that the proposed TLWFSVR model obtains higher intention understanding accuracy and less computational time than that of two-layer fuzzy support vector regression, support vector regression, and back propagation neural network (BPNN), respectively. Additionally, the preliminary application experiments are performed in the developing human-robot interaction system, called emotional social robot system, where 12 volunteers and 2 mobile robots experience a scenario of "drinking at a bar." Application results indicate that the bartender robot is able to understand customers' order intentions.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2018.2809691