Neural Compensation Research Articles

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application

Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.

A convolutional neural network model detecting lasting behavioral changes in mice with kanamycin-induced unilateral inner ear dysfunction

In acute aminoglycoside ototoxicity of the unilateral inner ear, physical abnormalities, such as nystagmus and postural alteration, are relieved within a few days by neural compensation. To examine exploratory behavior over an extended period, behaviors of freely moving mice after unilateral kanamycin injection into the inner ear were recorded in a home cage environment. The tail was excluded from deep learning-mediated object detection because of its delayed movement relative to the body. All detection results were confirmed using a convolutional neural network classification model. In kanamycin-injected mice, the total distance moved in 15 min increased on postoperative day 3. Furthermore, injured mice turned more frequently toward the healthy side up to 17 days after the surgery. This tendency resulted in increased clockwise movements in home cage recordings. Moreover, tail suspension and twisting toward the healthy side induced a physical sign for up to 14 days after the injury; the mice rapidly rotated with dorsal bending. Our analysis strategy employing deep learning helps to evaluate neuronal compensatory processes for an extended period and is useful for assessing the efficacy of therapeutic interventions.

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Inhibitory P300 subprocesses and neural compensation in genetic risk for Alzheimer's disease: The case for temporal-spatial principal component analysis.

The P300 event-related potential (ERP) is widely investigated in cognitive neuroscience, including related to aging, with smaller amplitudes and delayed latency consistently reported in Alzheimer's disease (AD). Given that AD-related neurological changes begin years before symptom onset, ERPs in asymptomatic elders with AD risk may characterize early changes. ERPs are seldom studied in this population. Yet, healthy carriers of apolipoprotein-E (APOE) ε4 have evidenced delayed P300 latencies, while P300 amplitude differences are seldom found. However, despite its frequent study, the specific cognitive processes reflected by P300 remain unclear. We propose that these challenges are due to the relatively long P300 window, which likely encompasses multiple underlying subprocesses that overlap in time. Temporal-spatial principal component analysis (tsPCA) maintains the high temporal resolution of EEG and is better suited to isolate processes that overlap in time. Thus, we interrogated APOE ε4 differences in P300 activity during successful stop-signal inhibitory control in healthy, cognitively intact older adults (25 ε4-, 20 ε4+), using both conventional ERP metrics (i.e., mean and peak amplitude) and P300 tsPCA factors. P300 amplitudes did not differ by ε4 using conventional metrics. tsPCA revealed two P300 factors in each ε4 group: first, a Posterior P300 (attention allocation) factor, and second, a relatively Anterior P300 (performance monitoring, evaluating, and updating) factor. tsPCA uniquely revealed greater activity in ε4+ vs. ε4- in Anterior P300. ε4 groups had comparable task performance, suggesting that greater P300 activity in ε4+ likely reflects neural compensation for ε4-related deficits, thereby enabling the maintenance of good task performance.

Multilayer neuroadaptive constraint-handling control architecture for a family of nonlinear systems with uncertainty compensation

In practical applications, there are some requirements for time-varying constraints on full system states. Currently, Barrier Lyapunov Function is a popular tool for constraint control. However, how to handle various uncertainties while satisfying constraint requirements is worth further research. Accordingly, a high-performance multilayer neuroadaptive constraint-handling controller for a class of nonlinear systems with uncertainty compensation will be developed. Significantly, asymmetric time-varying constraints for full system states can be implemented. Furthermore, a set of novel multilayer neuroadaptive disturbance observers will be proposed to address modeling uncertainties. Moreover, by introducing a set of nonlinear command filters, the intelligent control algorithm will be designed via the command filtered backstepping method. The stability of the whole closed-loop system is strictly proved. Additionally, the proposed algorithm is applied to different nonlinear systems including a hydraulic robotic manipulator system to verify its feasibility.

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Advances in drift compensation algorithms for electronic nose technology

Purpose This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues. Design/methodology/approach The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques. Findings The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction. Originality/value This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation

Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer’s disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Design of a novel intelligent adaptive fractional-order proportional-integral-derivative controller for mitigation of seismic vibrations of a building equipped with an active tuned mass damper

The primary objective of this study is to introduce a novel adaptive fractional order proportional–integral–derivative (FOPID) controller. The adaptive FOPID controller’s parameters are dynamically adjusted in real-time using five distinct multilayer perceptron neural networks. The extended Kalman filter (EKF) is employed to facilitate the parameter-tuning process. A multilayer perceptron neural network, trained using the error Backpropagation algorithm, is employed to identify the structural system and estimate the plant. The real-time estimated Jacobian is applied to the controller to control the model. The stability and robustness of the adaptive interval type-2 fuzzy neural networks controller are enhanced by utilizing the EKF and the feedback error learning strategy for compensator tuning. This improvement increases resilience against estimation errors, seismic disturbances, and unknown nonlinear functions. The primary objective is to address the challenges posed by maximum displacement, acceleration, and drift, as well as the uncertainties arising from variations in stiffness and mass. In order to validate the reliability of the proposed controller, the performance investigation is carried out on an 11-story building equipped with an active tuned mass damper under far and near-field earthquakes. Numerical findings show the remarkable effectiveness of the proposed controllers compared to their predecessors. In addition, it is revealed that the inclusion of the adaptive interval type-2 fuzzy neural networks compensator has increased the performance of the proposed controller and shows significant capabilities in reducing the seismic responses of structures during severe earthquake events.

Dynamic positioning system of ships with RBF neural network compensator

Abstract In Dynamic Positioning, traditional control methods lack stability, while neural networks and reinforcement learning have become research hotspots. By learning decision-making strategies from environmental observation data, we can adapt to environmental changes in real time and achieve optimal control effects. We also design a reward function to evaluate control performance. Dynamic inversion control is a control strategy that transforms nonlinear systems into linear systems. To address the issue of ship dynamic positioning, we propose a novel controller design method by combining RBF neural networks with dynamic inversion control. Its effectiveness has been verified through simulation experiments, demonstrating its potential and value in practical applications. This method will help improve the positioning accuracy of ships and optimize control performance.

Anti-windup design for supercavitating vehicle based on sliding mode control combined with RBF network

During the longitudinal motion of a supercavitating vehicle, the stability control problem is complicated because of the nonlinear planing force on the tail part. The dynamic model of a supercavitating vehicle in longitude plane is nonlinear, simultaneously, the control instructions of a supercavitating vehicle may exceed the physical limits of an actuator. Therefore, designing a longitudinal stability control system for a supercavitating vehicle, not only the treatment of nonlinear planing force, but also the physical constraints of the actuator should be considered. For the longitudinal motion model of supercavitating vehicle, a cascade model is proposed, which decomposes the longitudinal motion of supercavitating vehicle into two subsystems. Sliding mode control based on RBF neural network compensation is adopted in the controller design process, and RBF neural network is exploited to approach the deviation caused by actuator saturation. The proposed control method can effectively compensate the performance degradation caused by control variable saturation, and has strong robustness.

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

Dynamic Separation Model-Based Sliding Mode Control with Adaptive Neural Network Compensators for a Reluctance Actuator Motion System

Dynamic Separation Model-Based Sliding Mode Control with Adaptive Neural Network Compensators for a Reluctance Actuator Motion System

The neural compensation phenomenon in schizophrenia with mild attention deficits during working memory task

BackgroundWorking memory (WM) and attention are essential cognitive processes, and their interplay is critical for efficient information processing. Schizophrenia often exhibits deficits in both WM and attention, contributing to function impairments. This study aims to investigate the neural mechanisms underlying the relationship between WM impairments and attention deficits in schizophrenia. MethodsWe assessed the functional-MRI scans of the 184 schizophrenias with different attention deficits (mild=133; severe=51) and 146 controls during an N-back WM task. We explored their whole-brain functional connectome profile by adopting the voxel-wise degree centrality (DC). Linear analysis was conducted to explore the associations among attention deficit severity, altered DC, and WM performance in patients. ResultsWe observed that all patients showed decreased DC in the pre-supplementary area (pre-SMA), and posterior cerebellum compared to the controls, and schizophrenia patients with mild attention deficits showed decreased DC in the supramarginal gyrus, insula, and precuneus compared with the other 2 groups. DC values of the detected brain regions displayed U-shaped or inverted U-shaped curves, rather than a linear pattern, in response to increasing attention deficits. The linear analysis indicated that altered DC of the pre-SMA can modulate the relationship between attention deficits and WM performance. ConclusionThe U-shaped or inverted U-shaped pattern in response to increasing attention deficits may reflect a compensation mechanism in schizophrenia with mild attention deficits. This notion is also supported by the linear analysis that schizophrenia patients with mild attention deficits can improve their WM performance by increasing the DC value of the pre-SMA.

Guaranteed Performance Event-Triggered Adaptive Consensus Control for Multiagent Systems under Time-Varying Actuator Faults

A guaranteed performance event-triggered adaptive consensus control is established for uncertain multiagent systems under time-varying actuator faults. To eliminate the impact caused by actuator faults, an adaptive neural network compensation strategy is developed. Simultaneously, by implementing the asymmetric barrier Lyapunov function and transform function, a prescribed time consensus control with guaranteed performance, is constructed. Furthermore, to reduce the frequency of information transmission, an adjustable switching event-triggered control (ASETC) is proposed by using a modified hyperbolic tangent function. It combines the advantage of the relative threshold strategies and the characteristics of the hyperbolic tangent function, giving better flexibility in saving network resources and guaranteeing system performance. By applying the constructed control method, systems with prescribed performance consensus in a prescribed time are achievable while limited network resources and unknown time-varying faults are present. Some simulation examples implemented in MATLAB (R2022a) are given to demonstrate the above results.

Clearance Nonlinear Control Method of Electro-Hydraulic Servo System Based on Hopfield Neural Network

The electro-hydraulic servo system has advantages such as high pressure, large flow, and high power, etc., which can also realize stepless regulation, so it is widely used in many engineering machineries. A linear model is sometimes only a simple approximation of an idealized model, but in an actual system, there may be nonlinear and transient variation characteristics in the systems. Coupling is reflected in the fact that the components or functional structures implemented by each system used for the design of hydraulic systems are not completely or independently related to each other, but affect each other. The nonlinear clearance between the actuator and the load reduces the control accuracy of the system and increases the impact, thus losing stable working conditions. In the paper, based on the nonlinear clearance problem of the electro-hydraulic servo system, a mathematical transfer model with clearance is established, and on this basis, a clearance compensation method based on the Hopfield neural network is proposed. In this way, clearance compensation can be realized by adjusting the parameters of neural network nodes, through simple and convenient operation. Finally, by setting different clearance values, the results of the step response and sine response curve before and after clearance compensation of the hydraulic system are compared, and the effectiveness of Hopfield neural network compensation clearance control is verified based on the comparison simulation results.

Prefrontal cortical activity during uneven terrain walking in younger and older adults.

Walking in complex environments increases the cognitive demand of locomotor control; however, our understanding of the neural mechanisms contributing to walking on uneven terrain is limited. We used a novel method for altering terrain unevenness on a treadmill to investigate the association between terrain unevenness and cortical activity in the prefrontal cortex, a region known to be involved in various cognitive functions. Prefrontal cortical activity was measured with functional near infrared spectroscopy while participants walked on a novel custom-made terrain treadmill surface across four different terrains: flat, low, medium, and high levels of unevenness. The assessments were conducted in younger adults, older adults with better mobility function and older adults with worse mobility function. Mobility function was assessed using the Short Physical Performance Battery. The primary hypothesis was that increasing the unevenness of the terrain would result in greater prefrontal cortical activation in all groups. Secondary hypotheses were that heightened prefrontal cortical activation would be observed in the older groups relative to the younger group, and that prefrontal cortical activation would plateau at higher levels of terrain unevenness for the older adults with worse mobility function, as predicted by the Compensation Related Utilization of Neural Circuits Hypothesis. The results revealed a significant main effect of terrain, indicating a significant increase in prefrontal cortical activation with increasing terrain unevenness during walking in all groups. A significant main effect of group revealed that prefrontal cortical activation was higher in older adults with better mobility function compared to younger adults and older adults with worse mobility function in all pooled terrains, but there was no significant difference in prefrontal cortical activation between older adults with worse mobility function and younger adults. Contrary to our hypothesis, the older group with better mobility function displayed a sustained increase in activation but the other groups did not, suggestive of neural compensation. Additional findings were that task-related increases in prefrontal cortical activation during walking were lateralized to the right hemisphere in older adults with better mobility function but were bilateral in older adults with worse mobility function and younger adults. These findings support that compared to walking on a flat surface, walking on uneven terrain surfaces increases demand on cognitive control resources as measured by prefrontal cortical activation.

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Compound-choking theory and artificial neural networks-based hybrid modeling for supersonic ejectors

As the core component of ejector refrigeration systems, the research of ejector mechanisms has always been a hot topic. Based on the compound-choking theory, this paper studies the relationship between the entrainment ratio and critical back pressure and designs a neural network-based online error compensation mechanism for supersonic ejectors. Firstly, a thermodynamic model of the ejector is developed by combining the ideal gas model, the compound-choking theory, and the constant-pressure mixing theory. Then, the thermodynamic relationship between the entrainment ratio and critical back pressure is derived by introducing the idea of a hypothetical mixing cross section. Compared with the existing results, our method is able to improve critical back pressure prediction accuracy by virtue of the actual entrainment ratio. Therefore, a new adaptive error compensation algorithm is proposed for supersonic ejectors based on neural networks. Thus, the proposed scheme can be regarded as a hybrid modeling method for supersonic ejectors, which combines thermodynamic laws and data-driven artificial intelligence algorithms. Experimental data from ejector refrigeration systems with R141b, R245fa, water vapor and R134a are used in this paper to verify the validity of the model. The simulation results show that our method can effectively predict the critical back pressure, and all the prediction errors are less than 10%.