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Long short-term memory in recognizing behavior sequences on humanoid robot.

Anthropomorphic robots; Behavioral research; Brain; Complex networks; Deep learning; Gaussian noise (electronic); Intelligent computing; Intelligent systems; Network architecture; Soft computing; Behavior recognition; Behavior sequences; Humanoid; LSTM; Multi layer perceptron; Neural network model;...

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Main Authors: Neoh D., Mohamed Sahari K.S., Loo C.K.
Other Authors: 56942483000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-23762
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spelling my.uniten.dspace-237622023-05-29T14:51:38Z Long short-term memory in recognizing behavior sequences on humanoid robot. Neoh D. Mohamed Sahari K.S. Loo C.K. 56942483000 57218170038 55663408900 Anthropomorphic robots; Behavioral research; Brain; Complex networks; Deep learning; Gaussian noise (electronic); Intelligent computing; Intelligent systems; Network architecture; Soft computing; Behavior recognition; Behavior sequences; Humanoid; LSTM; Multi layer perceptron; Neural network model; Recurrent neural network (RNN); Time delay neural networks; Long short-term memory In order for robots to learn more complex behaviors, recognizing primitive behaviors plays a fundamental role. Research has shown that the recognition of primitive behaviors such as basic gestures enables robots to learn more complex behaviors as combinations of these simple, primitive behaviors. The focus of this study is to investigate the tolerance of neural network models to noisy inputs. We compare and evaluate several neural network architectures including the multilayer perceptron (MLP), time-delay neural network (TDNN), recurrent neural network (RNN) and the Long Short-Term Memory (LSTM). We show that the LSTM is superior to other models in terms of its robustness noisy inputs subjected to Gaussian noise. � 2018 IEEE. Final 2023-05-29T06:51:37Z 2023-05-29T06:51:37Z 2018 Conference Paper 10.1109/SCIS-ISIS.2018.00142 2-s2.0-85067129405 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067129405&doi=10.1109%2fSCIS-ISIS.2018.00142&partnerID=40&md5=3a75872b49d79e8c4f2b65ff52cd44fb https://irepository.uniten.edu.my/handle/123456789/23762 8716108 859 866 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Anthropomorphic robots; Behavioral research; Brain; Complex networks; Deep learning; Gaussian noise (electronic); Intelligent computing; Intelligent systems; Network architecture; Soft computing; Behavior recognition; Behavior sequences; Humanoid; LSTM; Multi layer perceptron; Neural network model; Recurrent neural network (RNN); Time delay neural networks; Long short-term memory
author2 56942483000
author_facet 56942483000
Neoh D.
Mohamed Sahari K.S.
Loo C.K.
format Conference Paper
author Neoh D.
Mohamed Sahari K.S.
Loo C.K.
spellingShingle Neoh D.
Mohamed Sahari K.S.
Loo C.K.
Long short-term memory in recognizing behavior sequences on humanoid robot.
author_sort Neoh D.
title Long short-term memory in recognizing behavior sequences on humanoid robot.
title_short Long short-term memory in recognizing behavior sequences on humanoid robot.
title_full Long short-term memory in recognizing behavior sequences on humanoid robot.
title_fullStr Long short-term memory in recognizing behavior sequences on humanoid robot.
title_full_unstemmed Long short-term memory in recognizing behavior sequences on humanoid robot.
title_sort long short-term memory in recognizing behavior sequences on humanoid robot.
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427588171661312
score 13.222552

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