Changes In Nonlinear Dynamics Research Articles

A novel soft sensor approach for industrial quality prediction based TCN with spatial and temporal attention

The complex industrial process is often characterized by strong multivariate coupling and nonlinear dynamic changes, which pose great challenges to modeling and prediction. Traditional deep learning methods are difficult to effectively capture spatiotemporal characteristics of industrial processes, resulting in poor prediction accuracy. To tackle this issue, we propose a novel end-to-end method named STA-TCN, which utilizes a temporal convolutional network (TCN) with both spatial and temporal attention mechanisms. The TCN uses causal and dilated convolutions to capture long temporal patterns in time series data. The spatial attention identifies the significance of different features, while the temporal attention focuses on crucial time steps. This design assigns adaptive weights to different features and emphasizes key moments to improve the accuracy of dynamic processes. We conduct experiments on two industrial datasets and show that the proposed STA-TCN method achieves significantly improved predictive performance compared to TCN for quality prediction of industrial processes. The results validate the effectiveness and robustness of the proposed method.

A Comprehensive Review of Artificial Intelligence and Machine Learning in Control Theory

Traditional control methods, such as proportional-integral-derivative (PID) controllers and linear-quadratic regulators (LQRs), have proven effective for linear and well-modeled systems. However, these methods often perform poorly in nonlinear, complex and dynamic environments. The paper aims to investigate the modern control systems by integrating artificial intelligence (AI) techniques, such as machine learning (ML), reinforcement learning (RL), deep learning, and fuzzy logic, to enhance their adaptive, robust, and predictive capabilities. And it reviews the literature and analyzes AI integration in control systems. The proposed strategies include supervised learning for trajectory optimization and fault detection, reinforcement learning for optimal control in dynamic environments, neural networks for complex nonlinear function approximation, and fuzzy logic for handling uncertainty and imprecise inputs. AI techniques significantly enhance the ability to tackle nonlinear problems and dynamic changes, demonstrating superior performance in applications like self-driving cars adapting to various road conditions and optimal energy distribution in smart grids. Despite the challenges of computational complexity, scalability, and the safety and reliability in the implementation of interpretable AI models, this paper suggests that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.

Landslide Deformation Analysis and Prediction with a VMD-SA-LSTM Combined Model

The evolution of landslides is influenced by the complex interplay of internal geological factors and external triggering factors, resulting in nonlinear dynamic changes. Although deep learning methods have demonstrated advantages in predicting multivariate landslide displacement, their performance is often constrained by the challenges of extracting intricate features from extended time-series data. To address this challenge, we propose a novel displacement prediction model that integrates Variational Mode Decomposition (VMD), Self-Attention (SA), and Long Short-Term Memory (LSTM) networks. The model first employs VMD to decompose cumulative landslide displacement into trend, periodic, and stochastic components, followed by an assessment of the correlation between these components and the triggering factors using grey relational analysis. Subsequently, the self-attention mechanism is incorporated into the LSTM model to enhance its ability to capture complex dependencies. Finally, each displacement component is fed into the SA-LSTM model for separate predictions, which are then reconstructed to obtain the cumulative displacement prediction. Using the Zhonghai Village tunnel entrance (ZVTE) landslide as a case study, we validated the model with displacement data from GPS point 105 and made predictions for GPS point 104 to evaluate the model’s generalization capability. The results indicated that the RMSE and MAPE for SA-LSTM, LSTM, and TCN-LSTM at GPS point 105 were 0.3251 and 1.6785, 0.6248 and 2.9130, and 1.1777 and 5.5131, respectively. These findings demonstrate that SA-LSTM outperformed the other models in terms of complex feature extraction and accuracy. Furthermore, the RMSE and MAPE at GPS point 104 were 0.4232 and 1.0387, further corroborating the model’s strong extrapolation capability and its effectiveness in landslide monitoring.

Research on Underwater Navigation Adaptation Area Classification Prediction Based on Support Vector Machine Model

This paper focuses on the selection of adaptation areas for gravity matching navigation in marine regions, using gravity anomaly benchmark data as the foundation. A Support Vector Machine (SVM) model is established, and the model is verified and studied for its complexity. Initially, the gravity anomaly benchmark data is cleaned and preprocessed to ensure the accuracy and consistency of the information. Subsequently, based on the principal component analysis (PCA) criterion, four gravity field characteristic parameters of the adaptability designation for each area are screened, resulting in two feature attribute indicators for judging regional adaptability: average gravity change and gravity field slope standard deviation. Through feature extraction from the refined gravity anomaly benchmark map, the classification of adaptation areas is predicted using a Support Vector Machine under non-linear separable conditions. Finally, for new gravity anomaly benchmark data, a translatability prediction is conducted on the classification system, achieving a good fitting effect. This indicates that System has significant applicability to new gravity anomaly data. It performs excellently in processing new gravity anomaly data, effectively interpreting and simulating the characteristics and patterns of these data. The system can accurately capture non-linear relationships and complex dynamic changes within the data, demonstrating good adaptability and robustness.

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Postnatal epigenome-mediated aging control and global trends

The epigenome can adequately regulate the on/off states of genes in response to external environmental factors and stress. In recent years, it has been observed that the epigenome, which is modulated through DNA methylation, histone modifications, and chromatin remodeling, changes with age. Alterations in the epigenome lead to the loss of cell-specific epigenome/identity, which in turn triggers a decline in tissue function. In mammals, postnatal epigenomic variations are not only caused by metabolic diseases, such as diabetes or DNA damage, but also by social stress and infectious diseases. Unlike Genome-Wide Association Studies (GWAS), dynamically changing epigenomes, along with their cellular roles, need to be established as objective biomarkers in conjunction with various biological signals, such as walking speed, brain waves, and clinical data. The biological age/aging clock, determined by methylated DNA, has attracted attention, and calorie restriction not only slows the progression of aging, but also seems to suppress it. However, as indicated by gene expression analysis in aging mice, aging is not a linear model, but is represented by nonlinear dynamic changes. Consequently, the development of experimental models and analytical methods that enhance temporal resolution through time-series analysis, tailored to spatial resolution, such as cell distribution and organ specificity, is progressing. Moreover, in recent years, in addition to anti-aging efforts targeting epigenomic variations, global attention has increasingly focused on research and development aimed at rejuvenating treatments, thus leading to the birth of many biotech companies. Aging Hallmarks such as inflammation, stem cells, metabolism, genomic instability, and autophagy, interact closely with the epigenome. Various postnatal and reversible epigenomic controls of aging, including Yamanaka factors (OKSM and OSK), are now entering a new phase. In the future, the development of aging control using diverse modalities, such as mRNA, artificial peptides, and genome editing, is expected, along with an improved molecular understanding of aging and identification of useful biomarkers.

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Tunnel settlement deformation monitoring is a complex task and can result in nonlinear dynamic changes. To overcome the disturbances caused by historical data and the difficulty in selecting input parameters during deformation prediction, a decomposition, reconstruction and optimization method for tunnel settlement deformation prediction is proposed. First, empirical mode decomposition (EMD) is used to decompose the in-situ monitoring data and reduce the interactions among information at different scales in sequences. Then, the monitoring data are decomposed into intrinsic mode functions (IMFs). Secondly, the smoothing factor of the generalized regression neural network (GRNN) is optimized by using the sparse search algorithm (SSA). An EMD-SSA-GRNN deformation prediction model is developed using the optimized GRNN algorithm and is used to predict the changes in the decomposed IMFs. Finally, using the measured deformation data from a shallowly buried tunnel along the Kaizhou-Yunyang Highway in Chongqing, China, the reliability and accuracy of different models are analysed. The results show that tunnel settlement deformation exhibited a trend and a slow change in the early stage, a rapid change in the middle stage and a slow change in the late stage, and the rate of change was significantly influenced by the excavation time and the upper and lower geological layers. The prediction accuracy of the EMD-SSA-GRNN model after EMD improved from 19.2 to 59.4% relative to that of the SSA-GRNN and single GRNN models. Moreover, we find that the three error evaluation indicators of the EMD-SSA-GRNN model are lower than those of the other models and that the results of the proposed model and are more strongly correlated with measured data.

Remote passive acoustic signal detection using multi-scale correlation networks and network spectrum distance in marine environment.

Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two networks, the method quantifies their similarity or dissimilarity, enabling comparisons of multi-scale correlation networks constructed from different time series data and tracking changes in nonlinear dynamics over time. The effectiveness of these methods is substantiated through comprehensive simulations and real-world data collected from the South China Sea. The results illustrate that the proposed approach attains a significant detection probability of over 90% when the signal-to-noise ratio exceeds -18 dB, whereas existing methods require a signal-to-noise ratio of at least -15 dB to achieve a comparable detection probability. This innovative approach holds promising applications in bolstering port security, facilitating coastal operations, and optimizing offshore activities by enabling more efficient detection of weak acoustic signals.

Three-hourly PM2.5 and O3 concentrations prediction based on time series decomposition and LSTM model with attention mechanism

In recent years, air quality has attracted wide attention from all over the world, among which the high concentration of particulate matter with an aerodynamic less than 2.5 μm (PM2.5) and ozone (O3) has a great adverse impact on human health and daily life. Previous studies on two pollutant predictions are overly concerned with model improvement but hardly focus on influence variable screening, and pollutant's time series features extraction and identification. To better improve the prediction accuracy and enhance the application of the model in practice, in the present study, a novel model RF-CEEMDAN-Attention-LSTM was proposed, which has three processes: (1) random forest (RF) screened out highly correlated influence variables; (2) complete ensemble empirical mode decomposition with adaptive noise (CEEMADN) method was adopted to decompose the PM2.5 and O3 concentration time series into multiple sub-time sequences; (3) the double hidden layer LSTM model with attention and dropout mechanism captured nonlinear relationships and dynamic changes of time series features. Three-hourly PM2.5 and O3 concentration in Chengdu were used to validate the effectiveness of the developed RF-CEEMDAN-Attention-LSTM model by comparing five other parallel models. The final results showed that the model not only had a better fitting effect on both PM2.5 and O3 than other comparable models in the entire timeline, but the model also had the highest R2 (PM2.5:0.916, O3:0.525) values for these two air pollutants at high concentration values.

Attitude Control of a Hypersonic Glide Vehicle Based on Reduced-Order Modeling and NESO-Assisted Backstepping Variable Structure Control

Aiming at solving the control problem caused by the large-scale change of the Hypersonic Glide Vehicle (HGV) parameters, this paper proposes a design method of backstepping variable structure attitude controller based on Nonlinear Extended State Observer (NESO), with the characteristics of HGV model and the idea of uncertainty estimation and compensation associated. Firstly, the design of the second-order NESO is studied. Due to the large number of NESO parameters, a systematic method for determining the second-order NESO parameters is given in this paper, and the stability of the observer is proved completely using the piecewise Lyapunov analysis. Then, the NESO-assisted backstepping variable structure attitude controller employs the reduced-order modeling idea to decompose the whole system design problem into two first-order subsystem design problem, and classifies the nonlinear dynamic changes caused by the large-scale changes of the aircraft parameters into the aggregated uncertain terms of the two subsystems. The simulation results show that the backstepping attitude controller based on NESO can realize the stable and accurate tracking of the flight attitude when the aircraft parameters change in a large range.

Transition characteristics of the dynamic behavior of a vehicle wheel-rail vibro-impact system

<abstract> <p>A two-degree-of-freedom vehicle wheel-rail impact vibration system model is developed, and the equivalent impact stiffness and damping of the rail are fitted applying ABAQUS, taking into account the high and low irregularity generated by the welded joints of the rail. A wheel-rail periodic interface with fixed impact was selected as the Poincaré map, and the fourth-order Runge-Kutta numerical method with variable step size was used to solve the system response. The dynamic characteristics of the system are investigated using a combination of the Bifurcation diagram, Phase plane diagram, the Poincaré map, the Time-domain diagram and the Frequency-domain diagram. It is verified that the vehicle wheel-rail impact vibration system has Hopf bifurcation, Neimark-Sacker bifurcation, Period-doubling bifurcation and Boundary crisis, and there are rich and complex nonlinear dynamic behavior changes. The research on the bifurcation and chaos characteristics of vehicle wheel-rail impact vibration systems can provide a reference for improving the stability of vehicle operation in engineering practice as well as the prediction and control of chaos in vehicle vibration reduction design.</p> </abstract>

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme.

Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We propose a hybrid intelligent scheme for physiological status prediction, which can be effectively utilized to predict the physiological status of patients and provide a reference for clinical diagnosis. Our proposed scheme initially extracted the attribute information of nonlinear dynamic changes in physiological signals. The maximum discriminant feature subset was selected by employing conditional relevance mutual information feature selection. An optimal subset of features was fed into the particle swarm optimization-support vector machine classifier to perform classification. For the prediction task, the proposed hybrid intelligent scheme was tested on the Sleep Heart Health Study dataset for sleep status prediction. Experimental results demonstrate that our proposed intelligent scheme outperforms the conventional machine learning classification methods.

Data-driven method for damage localization on soft robotic grippers based on motion dynamics.

Damage detection is one of the critical challenges in operating soft robots in an industrial setting. In repetitive tasks, even a small cut or fatigue can propagate to large damage ceasing the complete operation process. Although research has shown that damage detection can be performed through an embedded sensor network, this approach leads to complicated sensorized systems with additional wiring and equipment, made using complex fabrication processes and often compromising the flexibility of the soft robotic body. Alternatively, in this paper, we proposed a non-invasive approach for damage detection and localization on soft grippers. The essential idea is to track changes in non-linear dynamics of a gripper due to possible damage, where minor changes in material and morphology lead to large differences in the force and torque feedback over time. To test this concept, we developed a classification model based on a bidirectional long short-time memory (biLSTM) network that discovers patterns of dynamics changes in force and torque signals measured at the mounting point. To evaluate this model, we employed a two-fingered Fin Ray gripper and collected data for 43 damage configurations. The experimental results show nearly perfect damage detection accuracy and 97% of its localization. We have also tested the effect of the gripper orientation and the length of time-series data. By shaking the gripper with an optimal roll angle, the localization accuracy can exceed 95% and increase further with additional gripper orientations. The results also show that two periods of the gripper oscillation, i.e., roughly 50 data points, are enough to achieve a reasonable level of damage localization.

Research on Mono-Pulse Beam Angle Tracking Algorithm of Phased Array Antenna Based on CKF

Phased array antenna beam tracking and communication system based on mono-pulse system is an alternative satellite-to-earth communication technology solution for future planetary spacecraft, which meets the mission requirements of agile scanning, multimode, wide field of view, and small size. This paper firstly outlines the technical scheme of mono-pulse angle tracking based on the planar phased array antenna. A simplified mathematical model of angle measurement is summarized. Secondly, the beam pointing of the spacecraft communication antenna is frequently affected by the attitude and presents a nonlinear dynamic change. A robust two-dimensional angular tracking filtering algorithm based on cubature Kalman filter (CKF) is proposed, in order to solve this complex high-order nonlinear dynamic estimation problem. Finally, the comprehensive performance of the proposed algorithm is simulated and analyzed under two conditions of stable flight and high dynamic flight of the spacecraft. When the angular velocity is within 4°/s, the tracking accuracy can be better than 0.65°, and the tracking accuracy will be better than 1° when the angular velocity is within 8°/s. Compared with the basic CKF algorithm, the robust CKF tracking accuracy is significantly improved, while the resource consumption is slightly reduced. The research results can provide reference for the engineering implementation of related fields in the future.

A Neural Network NARMA-L2 Tracking Control for Electronic Throttle System*

<div class="section abstract"><div class="htmlview paragraph">In this paper, the Artificial Neural Network (ANN) control strategy based on the Nonlinear Auto Regressive Moving Average-Level 2 (<i>NARMA-L2</i>) technique has been used for tracking control of an electronic throttle body. The <i>NARMA-L2</i> nonlinear plant model is first identified offline by training using a set of input-output data pairs measured at different operating conditions. This data was collected from an actual operation of the throttle body running in a closed-loop control system on a prototype vehicle. The identified <i>NARMA-L2</i> plant model was then inverted and used to force the throttle output position to approximately track any reference inputs with multiple set-point changes at different operating conditions. The <i>NARMA-L2</i> model was reconfigured to be an equivalent model of a feed-forward controller that can cancel not only the actual dynamic behavior of the throttle body but also the nonlinearity effects. This type of controller has great potential to overcome the difficulty of developing an inverse dynamic model of the throttle body [<span class="xref">1</span>] and has the ability to compensate any un-modeled nonlinear dynamic changes that may be excited due to environmental changes. These changes can include load, temperature, humidity, and external disturbances with noise effects that may be introduced into the closed-loop control of the throttle body during real time operation in the vehicle. The resulting closed loop control system used the <i>NARMA-L2</i> architecture that have a multilayer perceptron neural network structure with the dynamic back-propagation algorithm becomes an implicit algebraic model that perfectly tracks any multiple set-point changes at different operating conditions. Testing results from real time implementation are provided to illustrate the new controller performance for tracking controls during actual operation on a prototype vehicle. The identified <i>NARMA-L2</i> neuro-controller shows more robustness and better performs as compared to other controllers developed in previous work [<span class="xref">1</span>-<span class="xref">3</span>].</div><div class="htmlview paragraph">* This work was done while the author was at IAV Automotive Engineering Inc, 15620 Technology Drive, Northville, MI 48168.</div><div class="htmlview paragraph">† Currently working with MAHLE Powertrain LLC, 14900 GalleonCourt, Plymouth, MI 48170.</div></div>

Estimation of potassium levels in hemodialysis patients by T wave nonlinear dynamics and morphology markers

ObjectiveNoninvasive screening of hypo- and hyperkalemia can prevent fatal arrhythmia in end-stage renal disease (ESRD) patients, but current methods for monitoring of serum potassium (K+) have important limitations. We investigated changes in nonlinear dynamics and morphology of the T wave in the electrocardiogram (ECG) of ESRD patients during hemodialysis (HD), assessing their relationship with K+ and designing a K+ estimator. MethodsECG recordings from twenty-nine ESRD patients undergoing HD were processed. T waves in 2-min windows were extracted at each hour during an HD session as well as at 48 h after HD start. T wave nonlinear dynamics were characterized by two indices related to the maximum Lyapunov exponent (λt, λwt) and a divergence-related index (η). Morphological variability in the T wave was evaluated by three time warping-based indices (dw, reflecting morphological variability in the time domain, and da and daNL, in the amplitude domain). K+was measured from blood samples extracted during and after HD. Stage-specific and patient-specific K+ estimators were built based on the quantified indices and leave-one-out cross-validation was performed separately for each of the estimators. ResultsThe analyzed indices showed high inter-individual variability in their relationship with K+. Nevertheless, all of them had higher values at the HD start and 48 h after it, corresponding to the highest K+. The indices η and dw were the most strongly correlated with K+ (median Pearson correlation coefficient of 0.78 and 0.83, respectively) and were used in univariable and multivariable linear K+ estimators. Agreement between actual and estimated K+ was confirmed, with averaged errors over patients and time points being 0.000 ± 0.875 mM and 0.046 ± 0.690 mM for stage-specific and patient-specific multivariable K+ estimators, respectively. ConclusionECG descriptors of T wave nonlinear dynamics and morphological variability allow noninvasive monitoring of K+ in ESRD patients. SignificanceECG markers have the potential to be used for hypo- and hyperkalemia screening in ESRD patients.

Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score

With evaluating the randomness and the nonlinear dynamic change of time sequence effectively, multiscale fuzzy distribution entropy (MFDE) is proposed to extract fault features from vibration signal. However, it is unsuitable for multivariate signals, which contain more abundant fault information. Through changing the coarse-grained and calculation process of MFDE, a novel entropy named as multivariate multiscale fuzzy distribution entropy (MMFDE) is proposed, which not only has the characteristics of MFDE, but also considers the within- and cross-channel correlations. By applying MMFDE to two kinds of simulation signals, the results show the proposed entropy is stable and reliable. A novel fault diagnosis method for rotating machinery is proposed by extracting fault features with MMFDE, selecting sensitive ones with Fisher score (FS), and identifying working state with support vector machine (SVM). Two experiments show the better diagnosis performance of the proposed rotating machinery fault diagnosis method than the related methods with the accuracy of 98.95% and 96.80% respectively.

Analysis of thermal-mechanical causes of abrasive belt grinding for titanium alloy

The abrasive belt grinding process is a highly nonlinear dynamic change process. The coupling action mechanism between mechanical field and thermal field during grinding is complex and variable. In this paper, firstly, the abrasive belt grinding experiment of titanium alloy was carried out to measure the change of temperature and force during the grinding process, and the Savitzky-Golay filter analysis was carried out on force signal. Secondly, the generation of grinding heat and force was described in detail, the equilibrium process of thermal-mechanical coupling was analyzed, and the finite element equation of thermal-mechanical coupling was deduced for abrasive belt grinding. Thirdly, the finite element simulation model of abrasive belt grinding is established to comprehensively analyze the change rule of thermal-mechanical coupling in the grinding process. Finally, the experimental results and simulation results of belt grinding are discussed and analyzed. The experimental results found that the grinding force changes are divided into three different stages, and the maximum surface temperature decreases to a certain extent with the grinding process. The simulation results show that the plastic deformation zone and “dead metal zone” are consistent with the theory in the grinding process. This study can provide a reference for relevant machining and further improve the grinding workpiece’s surface quality through thermal-mechanical optimization.

A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation.

Ca2+ spikes initiated in the distal trunk of layer 5 pyramidal cells (PCs) underlie nonlinear dynamic changes in the gain of cellular response, critical for top-down control of cortical processing. Detailed models with many compartments and dozens of ionic channels can account for this Ca2+ spike-dependent gain and associated critical frequency. However, current models do not account for all known Ca2+-dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We overcome these limitations in a minimal two-compartment biophysical model. In our model, a basal-dendrites/somatic compartment included fast-inactivating Na+ and delayed-rectifier K+ conductances, while an apical-dendrites/trunk compartment included persistent Na+, hyperpolarization-activated cation (I h ), slow-inactivating K+, muscarinic K+, and Ca2+ L-type. The model replicated the Ca2+ spike morphology and its critical frequency plus three other defining features of layer 5 PC synaptic integration: linear frequency-current relationships, back-propagation-activated Ca2+ spike firing, and a shift in the critical frequency by blocking I h Simulating 1000 synchronized layer 5 PCs, we reproduced the current source density patterns evoked by Ca2+ spikes and describe resulting medial-frontal EEG on a male macaque monkey. We reproduced changes in the current source density when I h was blocked. Thus, a two-compartment model with five crucial ionic currents in the apical dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials, such as the error-related negativity.SIGNIFICANCE STATEMENT A minimal model of layer 5 pyramidal cells replicates all known features crucial for distal synaptic integration in these neurons. By redistributing voltage-gated and returning transmembrane currents in the model, we establish a theoretical framework for the investigation of cortical microcircuit contribution to intracranial local field potentials and EEG. This tractable model will enable biophysical evaluation of multiscale electrophysiological signatures and computational investigation of cortical processing.

Event-Triggered Adaptive Fuzzy Fault-Tolerant Control for Stochastic Nonlinear Systems via Command Filtering

This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). In order to reduce the computational burden, the command filtering design technique is incorporated into the adaptive fuzzy event-triggered control strategy. Finally, the effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.