Simulation of variational Gaussian process NARX models with GPGPU

Krivec, Tadej; Papa, Gregor; Kocijan, Juš

doi:10.1016/j.isatra.2020.10.011

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…[ 42 ] The effect of model uncertainty on the results reduces the prediction and improves the rationality, accuracy, and efficiency of the common angle prediction model. [ 43 ] The current study records the EMG signal of the response by designing a new chaotic stimulation function and applying it to the musculocutaneous nerve. The stimulation signal and the EMG signal created by each stimulation are then applied to the NARX neural network to train and validate and retest the trained data and new data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of biceps muscle electromyogram signal using a NARX neural network

Rahatabad¹,

Khodadadi²,

Sheikhani³

et al. 2023

J Med Signals Sens

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Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.

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Section: Introductionmentioning

confidence: 99%

Prediction of biceps muscle electromyogram signal using a NARX neural network

Rahatabad¹,

Khodadadi²,

Sheikhani³

et al. 2023

J Med Signals Sens

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“…In particular, GP has been proven to be effective in improving the learning accuracy and the learning effectiveness of uncertainties and dependencies in low data regimes [ 12 ]. More recently, a non-parametric Gaussian process (GP) was proposed for modeling with quantifiable uncertainty and nonlinearity [ 13 , 14 ] based on implicit variance trade-off [ 15 , 16 ]. This bridges the system modeling and data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Modeling by Distributed Gaussian Processes in Multiagent Systems

Xin

Shi

2022

Sensors

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This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback–Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples.

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