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

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dc.contributor.authorNeoh D.en_US
dc.contributor.authorMohamed Sahari K.S.en_US
dc.contributor.authorLoo C.K.en_US
dc.contributor.authorid56942483000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid55663408900en_US
dc.date.accessioned2023-05-29T06:51:37Z
dc.date.available2023-05-29T06:51:37Z
dc.date.issued2018
dc.descriptionAnthropomorphic 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 memoryen_US
dc.description.abstractIn 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8716108
dc.identifier.doi10.1109/SCIS-ISIS.2018.00142
dc.identifier.epage866
dc.identifier.scopus2-s2.0-85067129405
dc.identifier.spage859
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067129405&doi=10.1109%2fSCIS-ISIS.2018.00142&partnerID=40&md5=3a75872b49d79e8c4f2b65ff52cd44fb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23762
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
dc.titleLong short-term memory in recognizing behavior sequences on humanoid robot.en_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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