Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Ali, Wasiq; Khan, Wasim; Raja, Muhammad Asif Zahoor; He, Yigang; Li, Yaan

doi:10.3390/e23050550

Cited by 14 publications

(10 citation statements)

References 54 publications

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“…The NARX model training process is equivalent to the optimal mapping between the current inputs and the next step prediction [23]. The validated VUT model will generate the necessary data for the empirical model.…”

Section: Training the Narx Model Using Real-world Route Datamentioning

confidence: 99%

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Alhanouti,

Gauterin

2024

WEVJ

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An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

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Section: Training the Narx Model Using Real-world Route Datamentioning

confidence: 99%

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Alhanouti,

Gauterin

2024

WEVJ

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“… 41 Given that the skeletal neuromuscular system is a time-varying nonlinear system, one-step forward prediction of the NARX model is introduced to construct a Gaussian process autoregression model that uses the prediction confidence interval to describe uncertainty. [ 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.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Rahatabad¹,

Khodadadi²,

Sheikhani³

et al. 2023

Journal of Medical Signals &Amp; Sensors

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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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“…Probabilistic methodologies via AI algorithms have been implemented exhaustively for a variety of linear/nonlinear systems arising in a spectrum of applications in social, economic, environment, physical, and engineering disciplines [13][14][15][16][17]. A few illustrations of paramount interest include ecological studies [18], acoustics [19], physics [20][21][22][23][24], bioinformatics [25][26][27][28][29][30][31], fluid dynamics [32][33][34][35], financial mathematics [36,37], and energy [38]. These motivational recent relevant and valuable reported articles inspired authors to investigate the intelligence computing paradigm for numerical treatment and analysis for EESs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

et al. 2021

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The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.

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Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Cited by 14 publications

References 54 publications

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

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