Achieving transparency in series elastic actuator of sharif lower limb exoskeleton using LLNF-NARX model

Zibafar, Ahmad; Ghaffari, Sahand; Vossoughi, Gholamreza

doi:10.1109/icrom.2016.7886771

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can also be used for nonlinear filtering, in which the target output is a noise-free version of the input signal [34]. In addition, NARX can be used in the modeling of nonlinear dynamic systems [35][36][37], being the output fed back to the input of the feedforward neural network as part of the standard NARX architecture because the true one is available during network training [24,27]. Or there is the possibility of creating a series-parallel architecture in which the true output is used instead of feeding back the estimated output [34].…”

Section: B Nonlinear Autoregressive Methods With External Inputmentioning

confidence: 99%

NARX neural network model for strong resolution improvement in a distributed temperature sensor

et al. 2018

View full text Add to dashboard Cite

This paper proposes an approach to process the response of a distributed temperature sensor using a nonlinear autoregressive with external input neural network. The developed model is composed of three steps: extraction of characteristics, regression, and reconstruction of the signal. Such an approach is robust because it does not require knowledge of the characteristics of the signal; it has a reduction of data to be processed, resulting in a low processing time, besides the simultaneous improvement of spatial resolution and temperature. We obtain total correction of the temperature resolution and spatial resolution of 5 cm of the sensor.

show abstract

Section: B Nonlinear Autoregressive Methods With External Inputmentioning

confidence: 99%

NARX neural network model for strong resolution improvement in a distributed temperature sensor

et al. 2018

View full text Add to dashboard Cite

show abstract

“…-This NN is predicting the time series very well [79], [80], and is widely used with nonlinear dynamic systems [81], [82].…”

Section: Narxnn -It Is a Recurrent Dynamic Nn And Itmentioning

confidence: 99%

Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

et al. 2023

View full text Add to dashboard Cite

His research interests include digital signal processing for communications, multimedia, image, and video coding, low-power wireless communications, one-bit ADC multiple-input multiple-output, underwater communication, index and spatial modulation, Li-Fi technology, and visible light communication. He is a Technical Committee Member of many international conferences and a Reviewer of many international conferences, journals, and transactions. Moreover, he was the General Co-Chair of the IEEE ITCE, in 2018. Mohammed Farrag (Member, IEEE) received the B.Sc. degree in electrical engineering and the M.Sc. degree in communication and electronics from Assiut University, Egypt, in 2001 and 2008, respectively, and the Ph.D. degree in communication and electronics engineering from the Egypt-Japan University of Science and Technology (E-JUST), in 2013.

show abstract

“…In addition, it predicts the time series in a very efficient way [34], [35]. This model is used widely with nonlinear systems [36], [37]. The NARXNN model consists of three layers, as indicated in Fig.…”

Section: Nonlinear Autoregressive Models With Exogenous Inputs (Narx)mentioning

confidence: 99%

Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques

Ikram,

Djilali,

Abdennasser

et al. 2023

Res. Eng. Struct. Mat.

View full text Add to dashboard Cite

Heat exchangers are utilized in a vast region of the process industry for heating and cooling. Long-term operation of heat exchangers results in decreased efficiency due to many problems, such as fouling. Therefore, the object of this research paper is to use three artificial intelligence techniques (feedforward neural networks-multilayer perceptron (FNN-MLP), nonlinear autoregressive networks with exogenous inputs (NARX), and support vector machines (SVM-RBF)) for predicting the fouling resistance in the tube and the shell heat exchanger in the preheating circuit of atmospheric distillation. The results summarize the high training as well as the predictive capacity of the "FFNN-MLP" model for predicting the fouling resistance in the heat exchanger with the highest coefficient of correlation (R = 0.99961) and the lowest root-mean-squared error (nRMSE = 1.0031%) for the testing phase, where the FNN-MLP network is superior to that provided using the SVM model (R = 0.9955 and nRMSE = 3.8652%). All the models of artificial networks and machine learning techniques used in the current work can be used to predict the fouling resistance in heat exchanger data with high accuracy. Despite this, the FNN-MLP model is the preferred model compared with the other proposed models, followed by the NARX model.

show abstract

Achieving transparency in series elastic actuator of sharif lower limb exoskeleton using LLNF-NARX model

Cited by 7 publications

References 14 publications

NARX neural network model for strong resolution improvement in a distributed temperature sensor

NARX neural network model for strong resolution improvement in a distributed temperature sensor

Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques

Contact Info

Product

Resources

About