Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

NL-fuzzy拓扑空间中的N-紧性

利用α-远域族定义了NL-fuzzy拓扑空间中的N-紧性,借助α-分子网,模糊滤子以及模糊滤子基的概念给出其等价刻画。证明了N-紧性具有拓扑不变性以及有限多个N-紧集的并仍为N-紧集等性质。

基于改进Fuzzy ART的自适应雷达信号分选

侦察接收机对获取的辐射源波形去交织以分离不同信号,称为信号分选,是电磁频谱战系统的核心技术.复杂电磁环境下脉冲流密度大、时域波形及诸域特征严重交叠,导致多数基于无监督模型的信号分选方法难以胜任.提出一种可自适应调整警戒阈值的模糊自适应共振理论(AVT fuzzy ART)聚类算法,基于对属性差异敏感的曼哈顿距离自适应调整警戒阈值,依据在线累积数据得出的辐射源瞬态聚类概率对警戒阈值动态加权.仿真结果表明,该方法能在无历史先验信息的条件下胜任多类别辐射源信号去交错.

建筑供应链视角下区块链技术应用影响因素分析:基于Fuzzy-DEMATEL-ISM模型

为促进区块链技术赋能建筑供应链数字化转型,从建筑供应链视角探究区块链技术应用影响因素的相互关系与作用方式。基于模糊决策实验室与解释结构模型先后界定因素属性特征、相互作用关系,划分层次结构,并从因素的中心度(M)、原因度(R),层次结构维度进行分析。结果表明:政策导向与法律体系、技术兼容性、技术功能特性、技术运营成本、企业规模等作为原因因素,对区块链技术应用具有驱动作用;竞争者行为、节点企业使用意愿、行业技术认知、专业基础设施、核心人才与技术能力、供应链结构模式等因素作为结果因素,可反映区块链实际应用情况;在建筑供应链的区块链技术应用过程中以改善结果因素为导向的促进策略更为有效。研究结论可为促进区块链在建筑行业中的应用提供策略参考。

矿井带式输送机液压拉紧Fuzzy-PID控制技术研究

针对矿井传统带式输送机拉紧系统响应速度慢、调节能力差、拉紧控制时变性和非线性等问题,提出了一种矿井带式输送机液压拉紧系统Fuzzy-PID控制(基于模糊算法的PID控制)方法。首先,根据液压拉紧装置建立数学模型,其次通过Matlab内置的Simulink仿真库分别对Fuzzy-PID控制器和PID控制器的液压拉紧系统进行仿真,得出输送带拉紧张力启动响应阶段和张力突变的调节响应图,并做出对比分析。最后,通过试验测试来验证算法模型的有效性。仿真结果表明:矿井带式输送机液压拉紧Fuzzy-PID控制系统不仅在启动阶段还有张力突变过程中都具有更好的稳态性能、更快的响应速度。在拉紧装置启动响应阶段的张力超调量降低了13.5%、到达期望值的时间缩短了0.5 s。在拉紧装置张力突变即模拟拉紧和松带阶段,当张力增加时,Fuzzy-PID控制器的调节速度缩短了0.4 s,超调量下降了4%。当张力减少时,Fuzzy-PID控制器的调节速度缩短了0.3 s,超调量降低了2%。试验结果表明:采用Fuzzy-PID控制的效果更佳优异稳定,且损耗更小。对比于PID控制,Fuzzy-PID控制效果更为良好,平均时间缩短31%且总体趋于稳定。对于矿井带式输送机这种连续运输作业的设备,Fuzzy-PID控制技术为矿井带式输送带平稳运行提供了一定保障,不仅减少了电能浪费,也降低了维护保养带式输送机的保养成本。

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.

Anthropogenic Influence on Decadal Changes in Concurrent Hot and Dry Events over China around the Mid-1990s

The frequency and duration of observed concurrent hot and dry events(HDEs) over China during the growing season(April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China(SEC), northern China(NC), and northeastern China(NEC). The frequency of HDEs averaged over China in the present day(PD,1994–2011) is double that in the early period(EP, 1964–81);the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model(MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes,suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas(GHG) concentrations dominates the simulated decadal changes,increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol(AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

Sex and racial disparities in non-alcoholic fatty liver disease-related cardiovascular events: National inpatient sample analysis (2019)

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)increases cardiovascular disease(CVD)risk irrespective of other risk factors.However,large-scale cardiovascular sex and race differences are poorly understood.AIM To investigate the relationship between NAFLD and major cardiovascular and cerebrovascular events(MACCE)in subgroups using a nationally representative United States inpatient sample.METHODS We examined National Inpatient Sample(2019)to identify adult hospitalizations with NAFLD by age,sex,and race using ICD-10-CM codes.Clinical and demographic characteristics,comorbidities,and MACCE-related mortality,acute myocardial infarction(AMI),cardiac arrest,and stroke were compared in NAFLD cohorts by sex and race.Multivariable regression analyses were adjusted for sociodemographic characteristics,hospitalization features,and comorbidities.RESULTS We examined 409130 hospitalizations[median 55(IQR 43-66)years]with NFALD.NAFLD was more common in females(1.2%),Hispanics(2%),and Native Americans(1.9%)than whites.Females often reported non-elective admissions,Medicare enrolment,the median age of 55(IQR 42-67),and poor income.Females had higher obesity and uncomplicated diabetes but lower hypertension,hyperlipidemia,and complicated diabetes than males.Hispanics had a median age of 48(IQR 37-60),were Medicaid enrollees,and had non-elective admissions.Hispanics had greater diabetes and obesity rates than whites but lower hypertension and hyperlipidemia.MACCE,all-cause mortality,AMI,cardiac arrest,and stroke were all greater in elderly individuals(P<0.001).MACCE,AMI,and cardiac arrest were more common in men(P<0.001).Native Americans(aOR 1.64)and Asian Pacific Islanders(aOR 1.18)had higher all-cause death risks than whites.CONCLUSION Increasing age and male sex link NAFLD with adverse MACCE outcomes;Native Americans and Asian Pacific Islanders face higher mortality,highlighting a need for tailored interventions and care.

EVENT联合思政教学模式在提升护理实习生人文关怀能力中的应用研究

目的探讨EVENT联合思政教学模式在提升护理实习生人文关怀能力中的应用效果。方法随机抽取2022年7月至2023年7月在甲状腺疝小儿外科进行实习的护生为研究对象,对照组采用常规教学模式,实验组在对照组的基础上采用EVENT联合思政教学模式。比较两组理论成绩、自主学习能力、工作投入度及人文关怀能力。结果教学后,实验组理论成绩、自主学习能力、工作投入度及人文关怀能力均高于对照组(P<0.05)。结论EVENT联合思政教学模式能提升护理实习生理论成绩、自主学习能力、工作投入度及人文关怀能力,值得在教学中推广应用。

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Grassland-type ecosystem stability in China differs under the influence of drought and wet events

Ecological stability is a core issue in ecological research and holds significant implications forhumanity. The increased frequency and intensity of drought and wet climate events resulting from climatechange pose a major threat to global ecological stability. Variations in stability among different ecosystemshave been confirmed, but it remains unclear whether there are differences in stability within the sameterrestrial vegetation ecosystem under the influence of climate events in different directions and intensities.China's grassland ecosystem includes most grassland types and is a good choice for studying this issue.This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify thedirections and intensities of different types of climate events, and based on Normalized DifferenceVegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measurestability). Based on a traditional regression model, standardized methods were integrated to analyze theimpacts of the intensity and duration of drought and wet events on vegetation stability. The resultsshowed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had thelowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at anintermediate level. Regarding the impact of the duration and intensity of climate events on vegetationecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainlyaffected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppewas mainly affected by intensity in wet environments, and duration had no significant impact. Ourconclusions can provide decision support for the future grassland ecosystem governance.

Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in order to thoroughly assess the relevance and efficacy of the suggested methodology.

Some convergence theorems of fuzzy concave integral on fuzzyσ-algebra

In this paper,we consider the extension of the concave integral from classical crispσ-algebra to fuzzyσ-algebra of fuzzy sets.Firstly,the concept of fuzzy concave integral on a fuzzy set is introduced.Secondly,some important properties of such integral are discussed.Finally,various kinds of convergence theorems of a sequence of fuzzy concave integrals are proved.

IJO Press Event Photos

Pof.G.H.Naumamn,Pesidet ofICO and Pror Bruce Spivey,Peidenr-Elet of iCO moct Prof Xiu-Wen Hu midle Chier Eaitor of JOIES a Woc 2006 in Sao Paulo,Brazil.

A Neuro T-Norm Fuzzy Logic Based System

In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.