Human activity recognition with different artificial neural network based classifiers

Çatalbaş, Burak; Catalbas, Bahadir; Morgül, Ömer

doi:10.1109/siu.2017.7960559

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Feedforward NNs are successfully applied to classification and object detection type of problems, see e.g. [34][35][36]. In detail, Krizhevsky et al [34], Redmon and Farhadi [36] showed successful utilization of convolutional neural networks (CNNs) in object classification and detection problems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Two-Legged Robot Motion Control With Recurrent Neural Networks

Catalbas

Morgül

2022

J Intell Robot Syst

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Legged locomotion is a desirable ability for robotic systems thanks to its agile mobility and wide range of motions that it provides. In this paper, the use of neural network-based nonlinear controller structures which consist of recurrent and feedforward layers have been examined in the dynamically stable walking problem of two-legged robots. In detail, hybrid neural controllers, in which long short-term memory type of neuron models employed at recurrent layers, are utilized in the feedback and feedforward paths. To train these neural networks, supervised learning data sets are created by using a biped robot platform which is controlled by a central pattern generator. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is examined under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the inclusion of recurrent layer provides smooth transition and control between stance and flight motion phases and L 2 regularization is beneficial for walking performance. Finally, the proposed hybrid neural network models are found to be more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training.

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