scholarly journals The Motion Controller Based on Neural Network S-plane Model for Fixed-Wing UAVs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Pengyun Chen ◽  
Guobing Zhang ◽  
Tong Guan ◽  
Meini Yuan ◽  
Jian Shen
Keyword(s):  
Neural Network ◽  
Motion Controller ◽  
Plane Model

Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel Robot for Accurate Motion Controller Design

2021 ◽  
Author(s):  
Saeed Rahimi ◽  
Hasan Jalali ◽  
Mohammad Reza Hairi Yazdi ◽  
Ahmad Kalhor ◽  
Mehdi Tale Masouleh
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Controller Design ◽  
Parallel Robot ◽  
Motion Controller

scholarly journals Design of Neural Network Mobile Robot Motion Controller

10.5772/7584 ◽  
2010 ◽  
Author(s):  
Jasmin Velagic ◽  
Nedim Osmic ◽  
Bakir Lacevic
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Motion ◽  
Motion Controller

scholarly journals Research on Road Roughness Based on NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yingjie Liu ◽  
Dawei Cui
Keyword(s):  
Neural Network ◽  
The Body ◽  
Identification Accuracy ◽  
Vertical Acceleration ◽  
Plane Model ◽  
The Road ◽  
The Neural Network ◽  
Road Roughness ◽  
Network Identification ◽  
Narx Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

scholarly journals Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2106 ◽  
Author(s):  
Linchu Yang ◽  
Ji’an Chen ◽  
Weihang Zhu
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Leap Motion ◽  
Motion Controller ◽  
Hand Gestures ◽  
Leap Motion Controller ◽  
Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition is one of the most significant tools for human–computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion Controller (LMC) is proposed. In addition, based on LMC, an efficient way to capture the dynamic hand gestures is identified. Dynamic hand gestures are represented by sets of feature vectors from the LMC. The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively. On the ASL dataset with 360 samples, the system achieves accuracies of 100% and 96.3% on the training and testing sets. On the ASL dataset with 480 samples, the system achieves accuracies of 100% and 95.2%. On the Handicraft-Gesture dataset, the system achieves accuracies of 100% and 96.7%. In addition, 5-fold, 10-fold, and Leave-One-Out cross-validation are performed on these datasets. The accuracies are 93.33%, 94.1%, and 98.33% (360 samples), 93.75%, 93.5%, and 98.13% (480 samples), and 88.66%, 90%, and 92% on ASL and Handicraft-Gesture datasets, respectively. The developed system demonstrates similar or better performance compared to other approaches in the literature.

An adaptive recurrent-neural-network motion controller for X-Y table in CNC Machine

2006 ◽  
Vol 36 (2) ◽  
pp. 286-299 ◽  
Author(s):  
Faa-Jeng Lin ◽  
Hsin-Jang Shieh ◽  
Po-Huang Shieh ◽  
Po-Hung Shen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Cnc Machine ◽  
Motion Controller

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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