Exogenous Input Research Articles

Remaining useful life estimation based on the hybrid Support Vector Regression with the Nonlinear Autoregressive with Exogenous Input (SVR-NARX)

The rotating machines took an important role in the industries and manufacturing technology, the continually using of these tools leads to its breakdown, which manages to several loess, including high economies loss. This paper aims to avoid the unexpected failure of those tools by estimating the Remaining Useful Life (RUL) of the ball bearing, for this sake a couple of methods namely Decision Tree (DT), and the hybrid Support Vector Regression (SVR) with the Nonlinear Autoregressive with Exogenous Input (NARX) named as SVR-NARX which is applied to determine the RUL, first Time Domain Features (TDF) are extracted from the raw vibration signal and then this TDF are selected using the DT method, after that the Discrete Wavelet Transform (DWT) is applied on the selected features to separate the high and low frequencies from the selected features, the extracted frequencies components (EFC) are used as input which are used to train and test the SVR-NARX, the obtained model is then used to determine the RUL, The online PRONOSTIA database is applied for the training and testing the SVR-NARX, the SVR-NARX is compared to its primitives the SVR and NARX trained and tested using the EFC and the original selected feature, the overall of the applied strategy indicate that the SVR-NARX trained by the EFC gave high results in terms of Root Mean Squared Error (RMSE=0.0090, 0.0085) and Factor of determination (R2 = 0.999, 0.997) for both training and testing respectively, the applied strategy gave high result which should be further considered for other machine related tasks.

Przewidywanie atlantyckiej południkowej cyrkulacji z wykorzystaniem nieliniowych metod identyfikacji systemu i modelu NARMAX

The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in the coupled ocean-climate system and in global climate change. The analysis of its own behaviour and the understanding its links to other climate dynamics is of paramount importance today as we encounter an increasing pressure to adapt to climate change. Due to the enormous complexity, it is almost impossible to establish accurate models, purely based on first-principle modelling approaches, that can perfectly represent the relationships between the AMOC and other dynamic climate parameters. Data-based or data-driven modelling methods, can therefore provide an attractive alternative solution. Systematic regular and continuous measurement of the AMOC time series began in April 2004. The main objective of the paper is to use the monthly data of the AMOC measured during April 2004-Febuary 2017, together with the North Atlantic Oscillation (NAO) index, and density anomalies of the Gulf of Mexico, Labrador Sea and Norwegian Sea, measured during the same period, to investigate and understand the quantitative relationship between the AMOC and four drivers (NAO and the three density anomaly variables). In doing so, nonlinear system identification methods and the Nonlinear AutoRegressive Moving Average with Exogenous input (NARMAX) method are employed to develop a quantitative model that relates the AMOC to the four drivers. Experimental results show that the derived nonlinear model skillfully captures and represents the dynamics of the AMOC based on the other four variables. One of the findings from this study is that the use of autoregressive variables can help improve the prediction of the AMOC.

Protease-Responsive Toolkit for Conditional Targeted Protein Degradation.

BioPROTACs are heterobifunctional proteins designed for targeted protein degradation. While they offer a potential therapeutic avenue for modulating disease-related proteins, the current strategies are static in nature and lack the ability to modulate protein degradation dynamically. Here, we introduce a synthetic framework for dynamic fine-tuning of target protein levels using protease control switches. The idea is to utilize proteases as an interfacing layer between exogenous inputs and protein degradation by modulating the recruitment of target proteins to E3 ligase by separating the two binding domains on bioPROTACs. By decoupling the external inputs from the primary protease layer, new conditional degradation phenotypes can be readily adapted with minimal modifications to the design. We demonstrate the adaptability of this approach using two highly efficient "bioPROTAC" systems: AdPROM and IpaH9.8-based Ubiquibodies. Using the TEV protease as the transducer, we can interface small-molecule and optogenetic inputs for conditional targeted protein degradation. Our findings highlight the potential of bioPROTACs with protease-responsive linkers as a versatile tool for conditional targeted protein degradation.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

A prior-knowledge-based time series model for heat demand prediction of district heating systems

Time series prediction methods are effective for predicting energy demands of district heating systems. Due to the thermal inertia of systems, historical heat load is usually applied as the most crucial input for prediction. However, it results in lateral errors (phase lags) between actual and predicted heat demand. To address this issue, a hybrid time series model is proposed based on autoregressive integrated moving average model and resistance–capacitance model. To reduce the lateral errors, estimated current and future heat demands are introduced as exogenous inputs of the autoregressive integrated moving average model, and they perform as prior knowledge. To generate estimated heat demand, a piecewise resistance–capacitance model is proposed, which considers various climatic and operating conditions. Lateral error of the hybrid model is decreased by 7.1% compared with conventional autoregressive integrated moving average model. Moreover, the hybrid model retains good post-hoc interpretability, which proves that exogenous inputs can effectively reduce lateral errors.

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.

Mental navigation in the primate entorhinal cortex

A cognitive map is a suitably structured representation that enables novel computations using previous experience; for example, planning a new route in a familiar space1. Work in mammals has found direct evidence for such representations in the presence of exogenous sensory inputs in both spatial2,3 and non-spatial domains4–10. Here we tested a foundational postulate of the original cognitive map theory1,11: that cognitive maps support endogenous computations without external input. We recorded from the entorhinal cortex of monkeys in a mental navigation task that required the monkeys to use a joystick to produce one-dimensional vectors between pairs of visual landmarks without seeing the intermediate landmarks. The ability of the monkeys to perform the task and generalize to new pairs indicated that they relied on a structured representation of the landmarks. Task-modulated neurons exhibited periodicity and ramping that matched the temporal structure of the landmarks and showed signatures of continuous attractor networks12,13. A continuous attractor network model of path integration14 augmented with a Hebbian-like learning mechanism provided an explanation of how the system could endogenously recall landmarks. The model also made an unexpected prediction that endogenous landmarks transiently slow path integration, reset the dynamics and thereby reduce variability. This prediction was borne out in a reanalysis of firing rate variability and behaviour. Our findings link the structured patterns of activity in the entorhinal cortex to the endogenous recruitment of a cognitive map during mental navigation.

Comparison of non-linear time series models (Beta-t-EGARCH and NARMAX models) with Radial Basis Function Neural Network using Real Data

This paper presents a comparison of three different non-linear time series modelling approaches: NARMAX (Non-linear Autoregressive Moving Average with Exogenous Inputs), Beta-t-EGARCH (Beta t Exponential Generalized Autoregressive Conditional Heteroscedasticity), and Radial Basis Function Neural Networks (RBFNN) applied to weekly stock market index data. We will explain three types of models and compare their compositions and structures. Then, we will show which model gives better predictions. To study series data, the comparison involved analysing the structure of the model and its errors in various time series models and summarising their findings. We divide the data into two parts: training data to structure the time series and testing. The training data tests the model's predictions. Then, we can analyse the model with the errors and the best deterrence predictions. After selecting the NARMAX and Beta-t-EGARCH models, we test them with specific criteria. The best choice is finding the model with the lowest average errors. For this study, we analysed the weekly average closing of the Aramco 2222 index from 15 December 2019 to 16 July 2023 and made 187 observations.

Effect of Fallow on Nitrogen Accumulation in Soil Profile and Shallow Groundwater in Cropland Around Fuxian Lake

Soil nitrogen accumulation in cropland and groundwater nitrogen pollution can be effectively alleviated by reducing exogenous nitrogen input, and fallow is an important measure for reducing exogenous nitrogen input. To explore the effects of fallow on nitrogen accumulation in the soil profile and shallow groundwater, the soil profile and shallow groundwater in cropland around Fuxian Lake were selected as research objects. The changes in nitrogen accumulation in the 0-100 cm soil profile and nitrogen concentration in shallow groundwater before (December 2017) and after (August 2020 and April 2021) fallow and their relationships were analyzed. The results showed that the content and storage of nitrogen in soil profiles were significantly reduced by fallow, and the contents of TN, ON, DTN, NO3--N, and NH4+-N in 0-30, 30-60, and 60-100 cm soil profiles after fallow decreased by 18.4 %-36.5 %, 16.1 %-26.8 %, 54.0 %-130.2 %, 59.5 %-90.8 %, and 60.1 %-110.6 %, respectively. The storages of TN, ON, DTN, NO3--N, and NH4+-N in 0-100 cm soil profiles before fallow were (17.20 ±0.97) t·hm-2, (15.50 ±1.23) t·hm-2, (0.68 ±0.06) t·hm-2, (266.8 ±31.17) kg·hm-2, and (18.7 ±3.04) kg·hm-2, respectively. However, their storages after fallow decreased by 25.5 %, 23.3 %, 44.7 %, 80.1 %, and 59.9 %, respectively. Fallow also changed the concentration and composition of different forms of nitrogen in shallow groundwater. The concentrations of TN, ON, NO3--N, and NH4+-N in groundwater after fallow decreased by 88.4 %, 82.7 %, 92.1 %, and 65.8 %, respectively, and ON/TN and NH4+-N/TN increased from 26 % and 6 % before fallow to 39 % and 17 % after fallow, respectively, whereas NO3--N/TN decreased from 61 % before fallow to 41 % after fallow. Changes in nitrogen concentrations and their forms in groundwater were closely related to DTN, NO3--N, and NH4+-N in the soil profile and pH, ORP, and DO in groundwater before and after fallow. Our study highlights that fallow effectively reduced nitrogen accumulation in cropland soil profiles, further alleviating nitrogen pollution in shallow groundwater, and was conducive to preventing the deterioration of water quality in plateau lakes.

Effects of Different Forms of Nitrogen Addition on Soil Physical and Chemical Properties and Microbial Community Structure of Perennial Alpine Cultivated Grassland

This study aimed to investigate the impact of different nitrogen forms on soil physicochemical properties and microbial community structure in perennial alpine cultivated grasslands, in order to provide scientific basis for developing nitrogen addition strategies for perennial alpine cultivated grasslands. In June 2022, a 4-year-old Qinghai grassland mixed with Poa pratensis Qinghai and Festuca sinensis Qinghai was established at the Bakatai Farm in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province. The study was conducted without fertilization as a control (CK), and three different forms of nitrogen treatments were set up, namely, U:urea (amide nitrogen), A:ammonium sulfate (ammonium nitrogen), and N:calcium nitrate (nitrate nitrogen); the nitrogen application rate for each treatment was 67.5 kg·(hm2·a)-1, and the composition and diversity of soil nutrients and microbial communities under different treatments were analyzed. The results showed that the input of exogenous ammonium nitrogen significantly increased NH4+-N content, AP content, and EC; amide nitrogen input significantly increased SOC content and TN content; and nitrate nitrogen input significantly increased NO3--N content, AN content, and TC content. Exogenous nitrogen input changed the structure of soil bacterial and fungal communities, as well as the relative abundance of dominant phyla and genera, but it did not significantly affect the alpha diversity of bacterial and fungal communities. Principal coordinate analysis (PCoA) showed that different forms of nitrogen addition had a significant impact on the Beta diversity of bacterial communities, whereas the impact on fungal communities was not significant. Redundancy analysis (RDA) indicated that nitrogen addition mainly changed the composition and structure of microbial communities through soil ammonium nitrogen. Overall, ammonium nitrogen fertilizer should be given priority in the soil remediation process of perennial cultivated grasslands in the Qinghai Tibet Plateau.

Characterization of Urinary N-Acetyltaurine as a Biomarker of Hyperacetatemia in Mice.

Acetate is an important metabolite in metabolic fluxes. Its presence in biological entities originates from both exogenous inputs and endogenous metabolism. Because the change in blood acetate level has been associated with both beneficial and adverse health outcomes, blood acetate analysis has been used to monitor the systemic status of acetate turnover. The present study examined the use of urinary N-acetyltaurine (NAT) as a marker to reflect the hyperacetatemic status of mice from exogenous inputs and endogenous metabolism, including triacetin dosing, ethanol dosing, and streptozotocin-induced diabetes. The results showed that triacetin dosing increased serum acetate and urinary NAT but not other N-acetylated amino acids in urine. The co-occurrences of increased serum acetate and elevated urinary NAT were also observed in both ethanol dosing and streptozotocin-induced diabetes. Furthermore, the renal cortex was determined as an active site for NAT synthesis. Overall, urinary NAT behaved as an effective marker of hyperacetatemia in three experimental mouse models, warranting further investigation into its application in humans.

Identification of heat exchanger process through Hammerstein and polynomial models

Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system’s behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

A dynamic hybrid model for efficient tuberculosis incidence rate prediction

ABSTRACT Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

Model-based prediction of water levels for the Great Lakes: a comparative analysis

This comprehensive study addresses the correlation between water levels and meteorological features, including air temperature, evaporation, and precipitation, to accurately predict water levels in lakes within the Great Lakes basin. Various models, namely multiple linear regression (MLR), nonlinear autoregressive network with exogenous inputs (NARX), Facebook Prophet (FB-Prophet), and long short-term memory (LSTM), are employed to enhance predictions of lake water levels. Results indicate that all models, except for FB-Prophet, perform well, particularly for Lakes Erie, Huron-Michigan, and Superior. However, MLR and LSTM show reduced performance for Lakes Ontario and St. Clair. NARX emerges as the top performer across all lakes, with Lakes Erie and Superior exhibiting the lowest error metrics—root mean square error (RMSE: 0.048 and 0.034), mean absolute error (MAE: 0.036 and 0.026), mean absolute percent error (MAPE: 0.021% and 0.014%), and alongside the highest R-squared value (R2: 0.977 and 0.968), respectively. Similarly, for Lake Huron-Michigan, NARX demonstrates exceptional predictive precision with an RMSE (0.029), MAE (0.022), MAPE (0.013%), and an outstanding R2 value of 0.995. Despite slightly higher error metrics, NARX consistently performs well for Lake Ontario. However, Lake St. Clair presents challenges for predictive performance across all models, with NARX maintaining relatively strong metrics with an RMSE (0.076), MAE (0.050), MAPE (0.029%), and R2 (0.953), reaffirming its position as the leading model for water level prediction in the Great Lakes basin. The findings of this study suggest that the NARX model accurately predicts water levels, providing insights for managing water resources in the Great Lakes region.

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.

Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.

How will global trade patterns evolve in the long run?

AbstractIn this paper, the evolution of global trade patterns until 2050 is projected with a recursive dynamic computable general equilibrium (CGE) model. Feeding the model with exogenous projections on macroeconomic, demographic, sectoral and trade cost variables, the evolution of trade patterns emerges endogenously from the model. The approach is innovative in both modelling approach and exogenous inputs. GDP growth emerges endogenously in the model because of diffusion of ideas as a result of international trade and trade cost changes are based on estimates of technology and trade policy changes. The projections indicate that (i) because of projected reductions in trade costs, trade will grow more than GDP, generating a global trade‐to‐GDP growth rate of 1.1; (ii) because of structural change, the global share of manufacturing trade falls from 64% in 2020 to 52% by 2050, whereas the share of services trade rises substantially from 24% to 38%; and (iii) because of technological catch‐up, the share in global trade of both developing and least‐developed countries (LDCs) will rise (with developing countries overtaking developed economies around 2035), the share of intra‐developed country trade will fall, whereas the share of intra‐developing country trade and those between developing and developed countries will rise.

Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.

Deep Learning based Model Predictive Controller on a Magnetic Levitation Ball System

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC).

A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.