Nonlinear Autoregressive Neural Network Research Articles

Fuzzy Neural Network Control for a Reaction Force Compensation Linear Motor Motion Stage

Technological progress and market expansion in the semiconductor industry require ultra-precise and high-speed processing and motion systems. However, even though reaction force compensation (RFC) linear motor motion stages help reduce the transmitted reaction force, the residual vibration of the movable magnet track after motion acts as a disturbance, affecting settling time and tracking performance. To address this issue, this study introduces a fuzzy neural network controller for an RFC linear motor motion stage. Since non-linear factors, such as frictional forces from guide rails, make it difficult to accurately model the system, a Nonlinear Autoregressive Neural Network (NARX) is used to identify the nonlinearity from displacement and servo control outputs. The fuzzy logic controller, which takes into account the error and error rate, is then combined with the inverse model controller of NARX. Finally, the fuzzy neural network controllers are integrated with the existing PIV (proportional-integral-velocity) control in feedforward configurations. The experiments demonstrate the potential performance improvements of the proposed approach over the PIV controllers.

Fuzzy Neural Network Control for a Reaction Force Compensation Linear Motor Motion Stage

Technological progress and market expansion in the semiconductor industry require ultra-precise and high-speed processing and motion systems. However, even though reaction force compensation (RFC) linear motor motion stages help reduce the transmitted reaction force, the residual vibration of the movable magnet track after motion acts as a disturbance, affecting settling time and tracking performance. To address this issue, this study introduces a fuzzy neural network controller for an RFC linear motor motion stage. Since non-linear factors, such as frictional forces from guide rails, make it difficult to accurately model the system, a Nonlinear Autoregressive Neural Network (NARX) is used to identify the nonlinearity from displacement and servo control outputs. The fuzzy logic controller, which takes into account the error and error rate, is then combined with the inverse model controller of NARX. Finally, the fuzzy neural network controllers are integrated with the existing PIV (proportional-integral-velocity) control in feedforward configurations. The experiments demonstrate the potential performance improvements of the proposed approach over the PIV controllers.

Thermal dynamics aspect identification of loop heat pipe with capillary tube wick using nonlinear autoregressive exogenous neural network

The loop heat pipe (LHP) has the potential to be used as a passive cooling system in small modular reactors. The research objective is to study the thermal dynamics of LHP with capillary tube wick using a non-linear autoregressive exogenous (NARX) based on a neural network. The neural network identification of LHP with capillary tube wick was carried out on the MATLAB platform. The experiment data obtained is used to identify the neural network of LHP with capillary tube wick. The temperature of the water as an evaporator heat source was varied at 60, 70, 80, and 90°C. The LHP was charged with demineralized water with a filling ratio of 100%. The air as a coolant in condenser section was blown at velocity of 2.5 m/s. The LHP was vacuumed with an initial pressure of 2690 Pa. The result confirmed that NARX based on the neural network model can predict the temperature of the condenser section with a given input set under the steady-state and transient conditions. The coefficient of determination is higher than 0.998 and Mean Square Error (MSE) is below 0.0082. The result obtained shows that the NARX neural network model can predict thermal dynamics phenomena in LHP quickly and precisely.

Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks.

Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named P[5]-1 and P[5]-2, were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories. The thin films of the synthesized macrocycles were coated by using the spin coating technique on a suitable substrate under optimum conditions. All compounds and the prepared thin film surfaces were characterized by NMR, Fourier transform infrared (FT-IR), elemental analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), and contact angle measurements. All vapor sensing measurements were performed via the surface plasmon resonance (SPR) optical technique, and the responses of the P[5]-1 and P[5]-2 thin-film sensors were calculated with ΔI/Io × 100. The responses of the P[5]-1 and P[5]-2 thin-film sensors to dichloromethane vapor were determined to be 7.17 and 4.11, respectively, while the responses to chloroform vapor were calculated to be 5.24 and 2.8, respectively. As a result, these thin-film sensors showed a higher response to dichloromethane and chloroform vapors than to other harmful vapors. The SPR kinetic data for vapors validated that a nonlinear autoregressive neural network was performed with exogenous input for the best molecular modeling by using normalized reflected light intensity values. It can be clearly seen from the correlation coefficient values that the nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model for dichloromethane converged more successfully to the experimental data compared to other gases. The correlation coefficient values of the dichloromethane modeling results were approximately 0.99 and 0.98 for P[5]-1 and P[5]-2 thin-film sensors, respectively.

Wholesale price forecasts of green grams using the neural network

PurposeAgriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.Design/methodology/approachIn order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.FindingsOur model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.Originality/valueUtilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions

Regenerative braking can effectively recover vehicle kinetic energy, but its energy conversion efficiency is low under low-speed conditions, and there is also the problem of premature exit from energy recovery due to insufficient reverse electromotive force. The cooperation of gearbox gears can increase the speed range for the motor to recover energy, but unreasonable shifting will cause fluctuations in braking force and affect the consistency of the braking feel. Therefore, this paper aims to collaboratively optimize energy recovery and braking force fluctuations during gear shifting. Firstly, based on the model of braking system and transmission, the influence of shift strategy on braking impact and energy recovery is studied. In view of the challenge of determining a shift strategy with uncertain target braking speeds, a speed prediction model reconstructed by the support vector regression (SVR) model and the hybrid nonlinear autoregressive neural network (NAR) is proposed. On the basis of NAR-SVR speed prediction, the coupling effect of braking impact force and energy recovery efficiency is considered, and the collaborative optimization of regenerative braking torque and shift time is solved through a multi-objective cuckoo search algorithm. The hardware-in-the-loop test results verified that under high-speed conditions, the braking energy recovery rate of the proposed strategy was increased by 47.06 %, and the peak braking impact was reduced by 61.4 %. This research can provide a reference for the brake downshift optimization strategy and regenerative braking research of vehicles with non-decoupled electro-hydraulic composite braking systems.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.

Using nonlinear auto-regressive with exogenous input neural network (NNARX) in blood glucose prediction

BackgroundPredicting of future blood glucose (BG) concentration is important for diabetes control. Many automatic BG monitoring or controlling systems use BG predictors. The accuracy of the prediction for long prediction time is a major factor affecting the performance of the control system. The predicted BG can be used for glycemia management in the form of early hypoglycemic/hyperglycemic alarms or adjusting insulin injections. Recent developments in continuous glucose monitoring (CGM) devices open new opportunities for glycemia management of diabetic patients. Many of those systems need prediction for long prediction horizons to avoid going through hypo or hyperglycemia.MethodsIn this article a nonlinear autoregressive exogenous input neural network (NNARX) is proposed to predict the glucose concentration for longer prediction horizons (PHs) than that was obtained previously with an established recurrent neural network (RNN). The proposed NNARX is a modified version from our previously published RNN with different initialization and building technique but has the same architecture. The modification is based on starting with building nonlinear autoregressive exogenous input model using MATLAB and train it, then close the loop to get NNARX network.ResultsThe results of using the proposed NNARX indicate that the proposed NNARX is better in prediction and stability than unmodified RNN as PH becomes higher than 45 minutes.ConclusionsModification in RNN building extends the ability of the prediction till 100 minutes. It performs statistically significant improvements in the FIT and RMSE values for 100 minutes prediction. It also decreases root mean squared error (RMSE) for both 45 and 60 minutes of prediction.

Predicting the dynamic behavior of a magnetocaloric cooling prototype via artificial neural networks

Although magnetocaloric cooling is considered a promising long-term alternative to vapor compression, recent prototype developments have not yet made this technology commercially competitive, primarily due to its high energy consumption and lack of cost-effective, long-term mechanically-chemically stable materials. To address the first issue and understand how the efficiency of magnetocaloric systems can be improved, dynamic models can offer valuable insights into their transient operation. This work focuses on the development of an artificial neural network with experimental data to model the dynamic operation of a magnetic refrigeration system. Through a design of experiments approach, we propose excitation signals for the identification experiment, involving five manipulated variables and one selected disturbance as inputs, with the output temperature of the cold manifold and power consumption as the target parameters. We chose a nonlinear autoregressive artificial neural network with exogenous inputs to model the transient operation of the system. The temperature model achieved R2 values of 0.995 and 0.955 for the 1-step and 90-step ahead predictions, respectively. Similarly, the power consumption model achieved R2 values of 0.988 and 0.949 for the 1-step and 90-step ahead predictions, respectively. These performance metrics were evaluated on the test sets that were not used for training the models, highlighting the robustness and accuracy of the models in both short-term and long-term predictions.

State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms

The exponential growth of electric and hybrid vehicles, now numbering close to 6 million on the roads, has highlighted the urgent need to address the environmental impact of their lithium-ion batteries as they approach their end-of-life stages. Repurposing these batteries as second-life batteries (SLBs) for less demanding non-automotive applications is a promising avenue for extending their usefulness and reducing environmental harm. However, the shorter lifespan of SLBs brings them perilously close to their ageing knee, a critical point where further use risks thermal runaway and safety hazards. To mitigate these risks, effective battery management systems must accurately predict the state of health of these batteries. In response to this challenge, this study employs time-series artificial intelligence (AI) models to forecast battery degradation parameters using historical data from their first life cycle. Through rigorous analysis of a lithium-ion NMC cylindrical cell, the study tracks the trends in capacity and internal resistance fade across both the initial and second life stages. Leveraging the insights gained from first-life data, predictive models such as the Holt–Winters method and the nonlinear autoregressive (NAR) neural network are trained to anticipate capacity and internal resistance values during the second life period. These models demonstrate high levels of accuracy, with a maximum error rate of only 2%. Notably, the NAR neural network-based algorithm stands out for its exceptional ability to predict local noise within internal resistance values. These findings hold significant implications for the development of specifically designed battery management systems tailored for second-life batteries.

Predictive modelling of flow dynamics in micropolar hybrid nanofluids subjected to magnetic dipole influence using nonlinear autoregressive artificial neural networks with exogenous input

In the present research article, we explore the intelligence-based numerical computation of a nonlinear autoregressive artificial neural network with exogenous input using the Levenberg-Marquardt algorithm (ANN-LMA) trained to analyze the unsteady two-dimensional micro-polar flow of an incompressible hybrid nanofluid (Al 2 O 3−Fe 3 O 4/Ethylene Glycol) under the impact of the magnetic field introduced by the magnetic dipole on a nonlinear stretched sheet. In the first step of the research, partial differential equations are converted into nonlinear ordinary differential equations using appropriate transmission and then solved numerically via the Successive Over Relaxation method after applying the finite difference method. The impact of numerous emerging parameters on the solutions is displayed graphically, and the physical significance is discussed. The results show that the concentration profile displays a dwindling trend for the Brownian motion parameter, and the opposite trend is witnessed for the thermophoretic parameter. Moreover, for increasing values of the dimensionless distance parameter and ferromagnetic interaction parameter, the temperature and velocity profiles exhibit the opposite tendency. In the second step of the study, we validated the Successive Over Relaxation outcomes with an ANN-LMA. For this purpose, the suggested ANN-LMA is trained for six different scenarios of the reference dataset points of the given flow model. The analysis shows that Successive Over Relaxation and LM-SNNs yield similar results, with a decreasing trend for the Brownian motion parameter and an opposite trend for the thermophoretic parameter.

Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models’ potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models’ performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.

AI-Based Control of Storage Capacity in High-Power-Density Energy Storage Systems, Used in Electric Vehicles

Exempting batteries from supplying power transients in Electric Vehicles (EVs) is beneficial to extend their useful lifespan. The adaptive capacity of High Power-density Energy Storage Systems (HPESSs) like ultracapacitors or high-speed Flywheel Energy Storage Systems (FESSs) could fulfill the targets in this context. This paper proposes a sizing/control methodology and real-time AI-based control of the storage capacity for the adaptive capacity HPESSs, used in EVs. The sizing approach consists of an optimal energy management strategy and a sizing algorithm applied to a Variable-Step HPESS (VS-HPESS). This methodology derives the battery/VS-HPESS power-split and sizes of the storage capacities. In addition, a Nonlinear Autoregressive Neural Network with eXogenous inputs (NARX-NN) is trained offline to switch the desirable capacity of the VS-HPESS in real-time operation. Finally, an experiment is designed to evaluate the proposed real-time control scheme, in which the EV power transients are emulated and applied to a dual-capacitance ultracapacitor as the VS-HPESS. The results confirm the capability of the proposed approach to meet the considered targets.

Price forecasting through neural networks for crude oil, heating oil, and natural gas

Building price projections of various energy commodities has long been an important endeavor for a wide range of participants in the energy market. We study the forecast problem in this paper by concentrating on four significant energy commodities. Using nonlinear autoregressive neural network models, we investigate the daily prices of WTI and Brent crude oil as well as the monthly prices of Henry Hub natural gas and New York Harbor No. 2 heating oil. We investigate prediction performance resulting from various model configurations, including training techniques, hidden neurons, delays, and data segmentation. Based on the investigation, relatively straightforward models are built that yield quite accurate and reliable performance. Specifically, performance in terms of relative root mean square errors is 1.96%/1.81%/9.75%/21.76%, 1.96%/1.80%/8.76%/14.41%, and 1.87%/1.78%/9.10%/16.97% for model training, validation, and testing, respectively, and the overall relative root mean square error is 1.95%/1.80%/9.51%/20.35% for the whole sample for WTI crude oil/Brent crude oil/New York Harbor No. 2 heating oil/Henry Hub natural gas. The outcomes of this projection might be used in technical analysis or integrated with other fundamental forecasts for policy analysis.

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BackgroundPreviously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.ResultsThe infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.ConclusionsWe propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Power sharing enhancement through a decentralized droop-based control strategy in an islanded microgrid

A decentralized intelligent droop-based control strategy is proposed in this paper for the enhancement of equal active and reactive power sharing in an islanded inverter-based microgrid. Droop control is mostly preferred because it does not need communication facilities for its implementation. However, the presence of different feeder impedances for the different distributed generators (DGs) in a microgrid make reactive power sharing inaccurate. Furthermore, as a result of considerable load changes and different droop characteristics for the DGs, the inverter's output voltage and frequency are altered and these in turn alter the reactive and active power sharing respectively. With these conditions, the generalized droop control (GDC) strategy fails to effectively share load reactive and active power among the DGs. In order to extenuate these challenges, this paper presents a nonlinear autoregressive exogenous neural network droop-based control (NARX-NN DBC) strategy which does not depend on the varying line impedances, droop gains for the different DGs and is less affected by fluctuating loads in the grid. A microgrid, made up of two DGs and a load is modelled in MATLAB/Simulink environment and validation of the proposed control strategy is done through many simulations. Within the simulation runtime, NARX-NN DBC yielded a maximum frequency percentage deviation of 0.46% from the nominal value of 50 Hz whereas GDC yielded 0.62%. Regarding the voltage, NARX-NN DBC gave maximum deviation of 0.026% meanwhile GDC gave 0.079% from the nominal value for 380 V. In addition, during 0.57–0.64 s with load active power demand of 4.6 kW, NARX-NN DBC registered 0.43% power sharing error whereas GDC registered 6.5%. On the other hand, during 0.57–0.64 s, with 5kVAr load power demand, NARX-NN DBC registered 0.2% whereas, GDC registered 2%. These obtained results clearly show that NARX-NN DBC strategy has a better performance compared to GDC strategy with respect to power sharing in an autonomous microgrid.

Huizhou resident population, Guangdong resident population and elderly population forecast based on the NAR neural network Markov model

<abstract> <p>We propose a nonlinear auto regressive neural network Markov model (NARMKM) to predict the annual Huizhou resident population, Guangdong resident population and elderly population in China, and improve the accuracy of population forecasting. The new model is built upon the traditional neural network model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The delay order and hidden layer number of neurons has a greater effect the prediction result of NAR neural network model. Therefore, we make full use of prior information to constrain and test when making predictions. We choose reasonable parameter settings to obtain more reliable prediction results. Three experiments are conducted to validate the high prediction accuracy of the NARMKM model, with mean absolute percentage error (MAPE), root mean square error (RMSE), <italic>STD</italic> and <italic>R</italic><sup>2</sup>. These results demonstrate the superior fitting performance of the NARMKM model when compared to other six competitive models, including GM (1, 1), ARIMA, Multiple regression, FGM (1, 1), FANGBM and NAR. Our study provides a scientific basis and technical references for further research in the finance as well as population fields.</p> </abstract>

Retracted: Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Retracted: Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Coordinated adaptive cruise control with integration of driving behaviors based on prediction for surrounding vehicles status

The adaptive cruise is an important vehicle control process for unmanned vehicles. This paper studies coordinated adaptive cruise control (ACC) of energy-saving in an intelligent networked environment, aiming to integrate control for common driving behaviors (cruising, following, lane-changing) by hierarchical control. The decision-making layer for integrating three driving behaviors is designed to improve the economy and passing time. Respectively, cruising strategy is optimized by considering efficient working area of motor; following strategy is constructed by dynamically adjusting the following distance with the state changes of the surrounding vehicles; lane-changing strategy is developed by considering the motor working efficiency of target speed. To improve the predictability of decision-making, non-linear autoregressive neural network (NARX) is introduced to predict the surrounding vehicle state. Furthermore, receiving the control reference from the decision layer, the controller is designed to realizes the tracking control of vehicle speed and following distance based on model predictive control (MPC), which is co-optimized for tracking, economy, and comfort. Eventually, the effectiveness of the coordinated ACC control is verified based on the hardware in the loop (HiL) test. The experiment results show that the vehicles with coordinated ACC can shorten the passing time and reduce the driving energy consumption to a certain extent, especially in the medium and low speed conditions.