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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Institute of Electrical and Electronics Engineers Inc.
2023
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| _version_ | 1833411627764940800 |
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| author | Neoh D. Mohamed Sahari K.S. Loo C.K. |
| author2 | 56942483000 |
| author_facet | 56942483000 Neoh D. Mohamed Sahari K.S. Loo C.K. |
| author_sort | Neoh D. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| format | Conference Paper |
| id | my.uniten.dspace-23762 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| 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 |
| spellingShingle | Neoh D. Mohamed Sahari K.S. Loo C.K. Long short-term memory in recognizing behavior sequences on humanoid robot. |
| title | 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_short | Long short-term memory in recognizing behavior sequences on humanoid robot. |
| title_sort | long short-term memory in recognizing behavior sequences on humanoid robot. |
| url_provider | http://dspace.uniten.edu.my/ |