Basis Of Model Research Articles

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning.

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

Decoding the dancing of the tongue: A model-based learning approach to phonetic targets in coarticulationa).

A model synthesizing average frequency components from select sentences in an electromagnetic articulography database has been crafted. This revealed the dual roles of the tongue: its dorsum acts like a carrier wave, and the tip acts as a modulation signal within the articulatory realm. This model illuminates anticipatory coarticulation's subtleties during speech planning. It undergoes rigorous, two-stage optimization: statistical estimation and refinement to depict carryover and anticipation. The model's base, rooted in physiological insights, deciphers carryover targets while its upper layer captures anticipation. Optimization has pinpointed unique phonetic targets for each phoneme, providing deep insights into virtual target formation during speech planning. These simulations, aligning closely with empirical data and marked by a mere 0.18 cm average error, along with extensive listening tests attest to the model's accuracy and enhanced speech synthesis quality.

On the effects of impulsivity and compulsivity on neural correlates of model-based performance

Impaired goal-directed behavior is associated with a range of mental disorders, implicating underlying transdiagnostic factors. While compulsivity has been linked to reduced model-based (MB) control, impulsivity has rarely been studied in the context of reinforcement learning despite its links to reward processing and cognitive control. This study investigated the neural mechanisms underlying MB control and the influence of impulsivity and compulsivity, using EEG data from 238 individuals during a two-step decision making task. Single-trial analyses revealed a modulation of the feedback-related negativity (FRN), where amplitudes were higher after common transitions and positive reward prediction error (RPE), indicating a valence effect. Meanwhile, enhanced P3 amplitudes after rare transitions and both positive and negative RPE possibly reflect surprise. In a second step, we regressed the mean b values of the effect of RPE on the EEG signals onto self-reported impulsivity and compulsivity and behavioral MB control (w). The effect of RPE on FRN-related activity was mainly associated with higher w scores, linking the FRN to MB control. Crucially, the modulation of the P3 by RPE was negatively associated with compulsivity, pointing to a deficient mental model in highly compulsive individuals.

Cognitive Mapping of Reachable Space in Humans

Reinforcement learning illustrates how people represent and learn large-scale environments for spatial navigation. However, it is unclear how “reachable” spaces are represented when performing manual tasks like chopping vegetables. To analyze the mechanisms underlying learning reachable space, we used inspiration from a recent study (de Cothi et al., 2022) to develop a haptic maze task where human participants reached to a target while avoiding invisible haptic obstacles in the environment. Participants reached with a robotic handle, generating contact forces to simulate the maze boundary, obstacle walls, and the floor, which supported the hand. Participants were blind to their hand position, while the target and maze boundary remained visible. Two conditions were implemented for each experiment variation; maze obstacles were invisible in the first condition, then became visible in the second. Audio feedback was given upon finding the target. We tested participants in 25 unique mazes, performing 10 trials within each maze. Two experimental procedures were employed: one with a fixed target and varying starting locations (18 participants), while the other had a fixed starting location and variable targets (10 participants). We simulated and compared the likelihoods of 3 different reinforcement learning models: model-based (MB), model-free (MF) and successor representation (SR). MB-simulated agents learned a map of the maze, planning the shortest route to the target. MF-simulated agents cached and updated the expected total future reward for each action using experience, pursuing the highest-reward actions. SR-simulated agents generated a “cognitive” (predictive) map between grids (states), integrating it with the target to plan actions. Results suggested that humans use a combination of MB and MF to learn reachable spaces. MB agents had higher likelihoods (better represented human behaviour) than MF agents for earlier trials, but MF had greater likelihoods for later trials. Meanwhile, SR learning was significantly worse at predicting participant behaviour.

Habits and the subexploration of better transportation options: A dual-system approach

In psychology, choices are influenced by either goal-directed or habitual systems, which are represented by model-based (MB) and model-free (MF) reinforcement learning algorithms. This paper introduces these concepts into the transportation literature and shows that individuals with habitual tendencies often fail to consider new, potentially superior, alternatives. Across two experiments, participants chose between two bus options, with a third, objectively superior option introduced in a later phase. In both experiments, participants engaging in habitual behavior were less likely to explore the new alternative. Furthermore, we found habitual tendencies were moderated by self-reported stress levels, which is consistent with previous results in the psychological literature. This implies that new transportation modes or routes may remain underused as travelers adhere to habitual patterns, disregarding the introduction of potentially better alternatives, and prompt for public policies that aim to promote goal-directed behavior.

Model-based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

Model-Based Algorithm for Flexible Power Point Tracking for Photovoltaic Participation in Primary Frequency Regulation

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems.

Monitoring domestic water consumption: a comparative study of model-based and data-driven end-use disaggregation methods

ABSTRACT Monitoring the water usage of different appliances and informing consumers about it has been shown to have an impact on their behavior toward drinking water conservation. The most practical and cost-effective way to accomplish this is through a non-intrusive approach, that locally analyzes data received from a flow sensor at the main water supply pipe of a household. In this work, we present two different methods addressing the challenges of disaggregating end-use consumption and classifying consumption events. The first method is model-based (MB) and uses a combination of dynamic time wrapping and statistical bounds to analyze four water end-use characteristics. The second, learning-based (LB) method is data-driven and formulates the problem as a time-series classification problem without relying on a priori identification of events. We perform an extensive computational study that includes a comparison between an MB and an LB method, as well as an experimental study to demonstrate the application of the LB method on an edge computing device. Both methods achieve similar F1 scores (LB: 71.73%, MB: 71.04%) with the LB being more precise. The embedded LB method achieves a slightly higher score (72.01%) while enhancing on-site real-time processing, improving security and privacy and enabling cost savings.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus.

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

The influence of aerobic exercise on model-based decision making in women with posttraumatic stress disorder

Individuals with PTSD often exhibit deficits in executive functioning. An unexplored aspect of neurocognitive functions associated with PTSD is the type of learning system engaged in during decision-making. A model-free (MF) system is habitual in nature and involves trial-and-error learning that is often updated based on the most recent experience (e.g., repeat action if rewarded). A model-based (MB) system is goal-directed in nature and involves the development of an abstract representation of the environment to facilitate decisions (e.g., choose sequence of actions according to current contextual state and predicted outcomes). The existing neurocognitive literature on PTSD suggests the hypothesis of greater reliance on MF vs MB learning strategies when navigating their environment. While MF systems may be more cognitively efficient, they do not afford flexibility when making prospective predictions about likely outcomes of different decision-tree branches. Emerging research suggests that an acute bout of aerobic exercise improves certain aspects of neurocognition, and thereby could promote the utilization of MB over MF systems during decision making, although prior research has not yet tested this hypothesis. Accordingly, the current study administered a lab-based two-stage Markov decision-making task capable of discriminating MF vs MB decision making, in order to determine if moderate-intensity aerobic exercise (either shortly after or 30-minutes after the exercise bout has ended) promotes greater engagement in MB behavioral strategies compared to light-intensity aerobic exercise in adult women with and without PTSD (N = 61). Results revealed that control women generally displayed higher levels of MB behavior that was further increased following immediate exercise, particularly moderate-intensity exercise. By contrast, the PTSD group generally displayed lower levels of MB behavior, and exhibited greater MB behavior when completing the task following moderate-intensity aerobic exercise compared to light-intensity aerobic exercise regardless of whether there was a short or long delay between exercise and the task. Additionally, women with PTSD demonstrated less impairment in MB decision-making compared to controls following moderate-intensity aerobic exercise. These results suggest that an acute bout of moderate-intensity aerobic exercise boosts MB behavior in women with PTSD, and suggests that aerobic exercise may play an important role in enhancing cognitive outcomes for PTSD.

Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography.

X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.

Using Geographic Information System Technology for Land-Use Modelling on Developing area of Tanah Datar Sumatera Barat

GIS technology has advanced rapidly in recent decades to become a standard tool for managing and analyzing spatial data to solve spatial problems. Spatial modelling is one of the GIS products that can be used as a reference for future policy development for effective spatial management. This research aims to create land use models for the Tanah Datar area of West Sumatra in 2025 and 2035. The model is created using the Cellular Automata method and remote sensing data. The land use data from 1989 and 2002 became the model's base year, and 2019 served as the basis for testing the model's accuracy. According to the modelling results, there are several trends in land use change. Dryland agriculture expanded significantly, indicating an improvement in the pattern of agricultural land use. This condition suggests that regional development should focus more on potential and good spatial management.

Cortical Grey Matter Mediates Increases in Model-Based Control and Learning from Positive Feedback from Adolescence to Adulthood.

Cognition and brain structure undergo significant maturation from adolescence into adulthood. Model-based (MB) control is known to increase across development, which is mediated by cognitive abilities. Here, we asked two questions unaddressed in previous developmental studies. First, what are the brain structural correlates of age-related increases in MB control? Second, how are age-related increases in MB control from adolescence to adulthood influenced by motivational context? A human developmental sample (n = 103; age, 12-50, male/female, 55:48) completed structural MRI and an established task to capture MB control. The task was modified with respect to outcome valence by including (1) reward and punishment blocks to manipulate the motivational context and (2) an additional choice test to assess learning from positive versus negative feedback. After replicating that an age-dependent increase in MB control is mediated by cognitive abilities, we demonstrate first-time evidence that gray matter density (GMD) in the parietal cortex mediates the increase of MB control with age. Although motivational context did not relate to age-related changes in MB control, learning from positive feedback improved with age. Meanwhile, negative feedback learning showed no age effects. We present a first report that an age-related increase in positive feedback learning was mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. Our findings indicate that brain maturation, putatively reflected in lower GMD, in distinct and partially overlapping brain regions could lead to a more efficient brain organization and might thus be a key developmental step toward age-related increases in planning and value-based choice.SIGNIFICANCE STATEMENT Changes in model-based decision-making are paralleled by extensive maturation in cognition and brain structure across development. Still, to date the neuroanatomical underpinnings of these changes remain unclear. Here, we demonstrate for the first time that parietal GMD mediates age-dependent increases in model-based control. Age-related increases in positive feedback learning were mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. A manipulation of motivational context did not have an impact on age-related changes in model-based control. These findings highlight that brain maturation in distinct and overlapping cortical regions constitutes a key developmental step toward improved value-based choices.

Examination of Pre-Service Science Teachers' Model Based-Content Knowledge and Knowledge of Students’ Understanding on Chemical Bonds

This study aims to examine pre-service science teachers' (PSTs') model-based (MB) content knowledge (CK) and knowledge of students' understanding (KSU) of chemical bonds. The participants of the study consist of 229 senior pre service science teachers. The study is a case study from qualitative research designs. The data of the study were collected by using the "Chemical Bonds Model Knowledge Test (CBMKT)". The obtained data were analyzed using the "Model Based-Content Knowledge Rubric (MB-CK Rubric)" and "Model Based-Knowledge of Students' Understanding Rubric (MB-KSU Rubric)". The findings showed that PSTs mostly have an understanding of wrong modeling and not being able to make any modeling in terms of MB-CK on chemical bonds. In addition, it has been determined that the majority of pre-service teachers have an invalid and weak level in terms of MB-KSU. These findings indicate that PSTs' professional knowledge of chemical bonds in their undergraduate education should be supported by model-based teaching.

Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning☆

Sample efficiency, which refers to the number of samples required for a learning agent to attain a specific level of performance, is central to developing practical reinforcement learning (RL) for complex and large-scale decision-making problems. The ability to transfer and generalize knowledge gained from previous experiences to downstream tasks can significantly improve sample efficiency. Recent research indicates that successor feature (SF) RL algorithms enable knowledge generalization between tasks with different rewards but identical transition dynamics. It has recently been hypothesized that combining model-based (MB) methods with SF algorithms can alleviate the limitation of fixed transition dynamics. Furthermore, uncertainty-aware exploration is widely recognized as another appealing approach for improving sample efficiency. An agent can efficiently explore to better understand an environment by tracking uncertainty about the value of each available action. Putting together two ideas of hybrid model-based successor feature (MB-SF) and uncertainty leads to an approach to the problem of sample efficient uncertainty-aware knowledge transfer across tasks with different transition dynamics or/and reward functions. In this paper, the uncertainty of the value of each action is approximated by a Kalman filter (KF)-based multiple-model adaptive estimation. This KF-based framework treats the parameters of a model as random variables. To the best of our knowledge, this is the first attempt at formulating a hybrid MB-SF algorithm capable of generalizing knowledge across large or continuous state space tasks with various transition dynamics while requiring less computation at decision time than MB methods. We highlight why previous SF-based methods are constrained to knowledge generalization across same transition dynamics, present our novel approach on a firm theoretical foundation, and design a set of demonstration tasks to empirically validate the effectiveness of our proposed approach. The number of samples required to learn the tasks was compared to recent SF and MB baselines. The results show that our algorithm generalizes its knowledge across different transition dynamics, learns downstream tasks with significantly fewer samples than starting from scratch, and outperforms existing approaches. We believe that our proposed framework can account for the computationally efficient behavioural flexibilities observed in the empirical literature and can also serve as a solid theoretical foundation for future experimental work.

Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation.

The initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus. By combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n=150) and validation cohorts (n=97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n=40, ChiCTR2100050288). Our model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p=0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p=0.025) with a more variable individualized dose (0.023-0.096mg/kg/day vs 0.045-0.057mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75±2.01 vs 6.05±3.35, p=0.001). Our genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients. National Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Power Generation Control Algorithm for the Participation of Photovoltaic Panels in Network Stability

The push for renewable energy and sustainable development has led to an ever-increasing integration of grid-tied photovoltaic (PV) systems. To maximize revenue, this resource generally operates in maximum power point trackers (MPPT) mode. However, to ensure grid stability and reliability, system operators will continue to introduce new requirements, eventually forcing PV plants to adhere to primary frequency regulation. To perform this task, PV plants will have to be capable of operating outside the MPP and varying their power production, to maintain an active power reserve, according to grid request. This article presents an innovative model-based (MB) tracking algorithm devoted to supporting power network regulation. Due to the updated formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable performance and accuracy of the new algorithm, providing power regulation capability in the range 20%–100% of the maximum available power. Moreover, the impact of the employment of constant reduction coefficients on the algorithm performances has been evaluated. Validation has been performed based on the data collected over an observation interval of more than six months. Due to its flexibility, this could be the basis for the participation of PV systems to frequency regulation.

Reducing Computational Cost During Robot Navigation and Human–Robot Interaction with a Human-Inspired Reinforcement Learning Architecture

We present a new neuro-inspired reinforcement learning architecture for robot online learning and decision-making during both social and non-social scenarios. The goal is to take inspiration from the way humans dynamically and autonomously adapt their behavior according to variations in their own performance while minimizing cognitive effort. Following computational neuroscience principles, the architecture combines model-based (MB) and model-free (MF) reinforcement learning (RL). The main novelty here consists in arbitrating with a meta-controller which selects the current learning strategy according to a trade-off between efficiency and computational cost. The MB strategy, which builds a model of the long-term effects of actions and uses this model to decide through dynamic programming, enables flexible adaptation to task changes at the expense of high computation costs. The MF strategy is less flexible but also 1000 times less costly, and learns by observation of MB decisions. We test the architecture in three experiments: a navigation task in a real environment with task changes (wall configuration changes, goal location changes); a simulated object manipulation task under human teaching signals; and a simulated human–robot cooperation task to tidy up objects on a table. We show that our human-inspired strategy coordination method enables the robot to maintain an optimal performance in terms of reward and computational cost compared to an MB expert alone, which achieves the best performance but has the highest computational cost. We also show that the method makes it possible to cope with sudden changes in the environment, goal changes or changes in the behavior of the human partner during interaction tasks. The robots that performed these experiments, whether real or virtual, all used the same set of parameters, thus showing the generality of the method.

Deep reinforcement learning for active control of a three-dimensional bluff body wake

The application of deep reinforcement learning (DRL) to train an agent capable of learning control laws for pulsed jets to manipulate the wake of a bluff body is presented and discussed. The work has been performed experimentally at a value of the Reynolds number Re∼105 adopting a single-step approach for the training of the agent. Two main aspects are targeted: first, the dimension of the state, allowing us to draw conclusions on its effect on the training of the neural network; second, the capability of the agent to learn optimal strategies aimed at maximizing more complex tasks identified with the reward. The agent is trained to learn strategies that minimize drag only or minimize drag while maximizing the power budget of the fluidic system. The results show that independently on the definition of the reward, the DRL learns forcing conditions that yield values of drag reduction that are as large as 10% when the reward is based on the drag minimization only. On the other hand, when also the power budget is accounted for, the agent learns forcing configurations that yield lower drag reduction (5%) but characterized by large values of the efficiency. A comparison between the natural and the forced conditions is carried out in terms of the pressure distribution across the model's base. The different structure of the wake that is obtained depending on the training of the agent suggests that the possible forcing configuration yielding similar values of the reward is local minima for the problem. This represents, to the authors' knowledge, the first application of a single-step DRL in an experimental framework at large values of the Reynolds number to control the wake of a three-dimensional bluff body.

Model-Based Adaptive Event-Triggered Tracking Control of Discrete-Time Nonlinear Systems Subject to Strict-Feedback Form

The consumption of communication resources is an essential issue when control tasks are implemented in a wireless network environment. In order to lessen the network resources, a novel model-based (MB) adaptive event-triggered (ET) tracking control scheme is put forward in this article for strict-feedback discrete-time nonlinear systems. In this article, an event-based adaptive model is constructed by the combination of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step-ahead predictor and event-sampled neural networks. Then, the adaptive neural model is used for designing the MB ET controller. Besides, a modified ET condition is constructed without any delay. By combining a decoupled backstepping framework, the reverse Lyapunov stability technology is developed to verify the ultimate boundedness of all closed-loop signals and the convergence of the tracking error. Compared to the zero-order hold method, which keeps transmitted state signals unchanged in the interevent period, the proposed MB ET control scheme can keep the real-time update of state signals transmitted to the controller. It means that the triggering error will be smaller by the MB trigger mechanism, thereby improving the event-based tracking performance and further saving communication resources. Comparisons of simulation results are given to verify the effectiveness of the proposed control scheme.