Sampling Interval Research Articles

An Aperiodic-Sampling-Dependent Event-Triggered Control Strategy for Interval Type-2 Fuzzy Systems: New Communication Scheme and Discontinuous Functional.

This article studies the event-triggered control problem of interval type-2 (IT2) fuzzy systems. It proposes a new aperiodic-sampling-dependent event-triggered communication scheme, and introduces an improved sampling-dependent discontinuous functional. First, taking into account that the system is with aperiodic sampling, the weighting matrices used for judgement in the event-triggered scheme are designed to be related to each sampling interval or its upper and lower bounds, according to the sampling interval being predictable or not. This design makes the scheme more flexible, thereby improving control effectiveness. Second, a sampling-dependent discontinuous functional is introduced into event-triggered control, which contains matrix variables depending on the aperiodic sampling, and incorporates an improved discontinuous term and a vector related to event-triggered condition. Thus, the functional reduces the conservativeness of stability and stabilization criteria. Based on the above aperiodic-sampling-dependent strategy, the derived results achieve better event-triggered control effects compared with the recent literature. At the end, two examples are given to validate the theoretical effectiveness and advantages of the results under various parameters.

Sampled-data static output feedback robust MPC for LPV systems with bounded disturbances

This paper investigates a sampled-data static output feedback robust model predictive control (MPC) approach for continuous-time linear parameter varying (LPV) systems with unknown system states and bounded disturbances. Between two successive sampling time instants, it is assumed that the controlled LPV systems evolve in continuous-time, and the control inputs are piecewise constant. The robust stability conditions and control input constraints on the closed-loop system are considered in the formulated robust MPC optimization. The constraints in the formulated robust MPC optimizations are formulated as linear matrix inequalities (LMIs) and solved as convex optimization. The designed sampled-data static output feedback robust MPC optimization has a simple structure and directly optimizes output feedback controller. Furthermore, the system state constraint sets are on-line updated over the sampling interval. The designed sampled-data static output feedback robust MPC approach has the property of recursive feasibility, and can guarantee robust stability of the controlled continuous-time LPV systems. A simulation example is given to illustrate the effectiveness of the approach.

Arterial blood pressure monitoring in stroke cohorts: the impact of reduced sampling rates to optimise remote patient monitoring.

Remote patient monitoring (RPM) beat-to-beat blood pressure (BP) provides an opportunity to measure poststroke BP variability (BPV), which is associated with clinical stroke outcomes. BP sampling interval (SI) influences ambulatory BPV, but RPM BP SI optimisation research is limited. SI and RPM device capabilities require compromises, meaning SI impact requires investigation. Therefore, this study assessed healthy and stroke subtype BPV via optimised BP sampling, aiding sudden BP change identification and potentially assisting cardiovascular event (recurrent stroke) prediction. Leicester Cerebral Haemodynamic Database ischaemic [acute ischaemic stroke (AIS), n = 68] and haemorrhagic stroke (intracerebral haemorrhage, n = 12) patient and healthy control (HC, n = 40) baseline BP data were analysed. Intrasubject and interpatient SD (SDi/SDp) represented individual/population variability with synthetically altered SIs. Matched-filter approaches using cross-correlation function detected sudden BP changes. At SIs between 1 and 180 s, SBP and DBP SDi staticised while SDp increased at SI < 30 s. Mean BP and HR SDi and SDp increased at SI < 60s. AIS BPV, normalised to SI1s, increased at SI30s (26%-131%) and SI120s (1%-274%). BPV increased concomitantly with SI. Cross-correlation analysis showed HC and AIS BP sudden change detection accuracy reductions with increasing SI. Positive BP deviation detection fell 48.48% (SI10s) to 78.79% (SI75s) in HC and 67.5% (SI10s) to 100% (SI75s) in AIS. Negative BP deviation detection fell 50% (SI10s) to 82.35% (SI75s) in HC and 52.27% (SI10s) to 95.45% (SI75s) in AIS. Sudden BP change detection and BPV are relatively robust to SI increases within certain limits, but accuracy reductions generate unacceptable estimates, considerable within RPM device design. This research warrants further SI optimisation.

Quality of life among people with mental illness attending a psychiatric outpatient clinic in Ethiopia: a structural equation model.

Mental illness is one of the most severe, chronic, and disabling public health problems that affects patients' Quality of life (QoL). Improving the QoL for people with mental illness is one of the most critical steps in stopping disease progression and avoiding complications of mental illness. Therefore, we aimed to assess the QoL and its determinants in patients with mental illness in outpatient clinics in Northwest Ethiopia in 2023. A facility-based cross-sectional study was conducted among people with mental illness in an outpatient clinic in Ethiopia. The sampling interval was decided by dividing the total study participants who had a follow-up appointment during the data collection period (2400), by the total sample size 638, with the starting point selected by lottery method. The interviewer-administered WHOQOL BREF-26 tool was used to measure the quality of life (QoL) of people with mental illness. The domains of QoL were identified, and indirect and direct effects of variables were calculated using structural equation modelling with SPSS-28 and Amos-28 software. A p-value of < 0.05 and a 95% CI were used to evaluate statistical significance. A total of 636 (99.7%) participants agreed to participate and completed the data collection. The mean score of overall QoL of people with mental illness in the outpatient clinic was 49.6 ± 10 Sd. The highest QoL was found in the physical health domain (50.67 ± 9.5 Sd), and the lowest mean QoL was found in the psychological health domain (48.41 ± 10 Sd). Rural residence, drug nonadherence, suicidal ideation, not getting counselling, moderate or severe subjective severity, family does not participate in patient care and a family history of mental illness had an indirect negative effect on QoL. Alcohol use and psychological health domain had direct positive effect on QoL. Furthermore, objective severity of illness, having low self-esteem, and having history of mental illness in the family had both direct and indirect effect on QoL. Furthermore, sociodemographic factors (rural residence, illiterate educational status, not married marital status), social support-related factors (poor self-esteem, family not participating in patient care), substance use factors (alcohol use, tobacco use) and clinical factors (high objective and subjective severity of illness, not getting counselling, suicidal ideation, higher number of episodes, comorbid illness, family history of mental illness, poor drug adherence) directly and indirectly affected QoL. In this study, the QoL of people with mental illness was poor, with the psychological health domain the most affected. Sociodemographic factors, social support-related factors, drug use factors, and clinical factors, directly and indirectly affected QoL through the mediator variables of physical health domains, psychological health domains, social relation health domains, and environmental health domains. In order to improve the QoL of people with mental illnesses, we recommend that emphasis be given to addressing the QoL of those with mental illness, including the development of policy and practice responses that address the above identified factors.

The effect of severe renal impairment on the pharmacokinetics, safety and tolerability of mitiperstat.

Mitiperstat is a novel, highly potent myeloperoxidase inhibitor being evaluated in patients with cardio-metabolic disease (phase 2). These patients often have impaired renal function, which may affect mitiperstat pharmacokinetics. This study assessed mitiperstat pharmacokinetics, safety and tolerability in participants with severe renal impairment and normal renal function, to inform inclusion of participants with estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 in phase 3. Participants with severe renal impairment (eGFR ≥15 and <30 mL/min/1.73 m2) who were not on dialysis (n = 10) and group-matched controls (eGFR ≥90 mL/min/1.73 m2; n = 10) received a single mitiperstat 2.5mg oral tablet. Blood samples were collected at intervals for 2weeks and urine samples for 24 h post-dose. Total apparent mitiperstat clearance was 10.83 L/h in the severe renal impairment cohort and 25.62 L/h in the control cohort. The area under the plasma concentration-time curve was 2.37-fold higher (90% confidence interval [CI]: 1.79, 3.12) in the severe renal impairment cohort than in the control cohort, with longer elimination half-life and similar maximum concentration. Non-renal clearance was similar between the cohorts. Mitiperstat apparent clearance was approximately twofold lower in individuals with severe renal impairment than in those with normal renal function. Lower clearance was driven by reduced renal clearance; non-renal clearance was similar. Mitiperstat was generally well tolerated by participants with severe renal impairment and normal renal function. These findings, together with efficacy and safety/tolerability data from phase 2b, will guide the dosing regimen for phase 3.

A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records

Tridacna shells have become valuable archives for high-resolution paleoclimate reconstructions due to their daily-to-annual bands. The δ18O, influenced by temperature and salinity, serves as a key geochemical proxy for Tridacna. Traditionally, linear interpolation has been the go-to method for creating evenly spaced Tridacna δ18O timeseries. However, the accuracy of this method can be influenced by the prominent negative shell growth trends. Given that Tridacna shells exhibit significantly faster growth rates during the juvenile stage than in adulthood, constant sampling intervals may lead to overly dense sampling during the juvenile phase, which could potentially introduce errors in the linearly interpolated Tridacna δ18O record. In this study, we proposed a time window averaging method to mitigate the influence of growth trends in Tridacna δ18O records from the northern South China Sea (SCS). The time window averaging method calculates the average of measured Tridacna δ18O data within a time window, whose length is determined by the sampling resolution for each year and the expected temporal resolution. Pseudo-Tridacna experiments, using the newly developed proxy system model for Tridacna δ18O, were conducted to assess the accuracy of both interpolation and averaging methods. Results indicate that the averaging method outperforms the interpolation method, especially in seasonality estimation. The pseudo-Tridacna experiments recommend employing the averaging method for monthly Tridacna δ18O records when sampling density exceeds ∼20 samples/year. Two real Tridacna δ18O records from the northern SCS demonstrate consistency with pseudo-Tridacna experiments, supporting the efficacy of the averaging method in reducing bias in Tridacna δ18O records associated with growth trends. Our results show the potential of the time window averaging method to improve Tridacna-based paleoclimate reconstructions.

A novel adaptive event‐triggered security consensus control mechanism for leader‐following multi‐agent systems under hybrid random cyber attacks

AbstractAiming at the security consensus control problem of leader‐following multi‐agent systems (MASs) under hybrid random cyber attack, this article proposes a novel sampled information related adaptive event‐triggered control mechanism (SIRAETCM). While ensuring the safety performance of the MASs, the mechanism adaptively and dynamically adjusts the trigger threshold of every agent to achieve discontinuous communication by using only the current and latest sampled signals. According to the MASs communication mode and Bernoulli attack model, a security consensus control protocol is constructed, and a bilateral sampled‐interval Lyapunov functional (BSILF) method is introduced to obtain more sampling interval information and establish sufficient conditions for the leader‐following state error system to stabilize asymptotically under hybrid random cyber attacks. Meanwhile, under a large sampling interval, the controller gain and adaptive event‐triggered parameters are designed and obtained. The simulation of the tunnel diode circuit system shows that the SIRAETCM can reduce the number of communications between agents to improve bandwidth utilization, and the adopted safety cooperative control protocol can improve the safety and effectiveness of the MASs.

Hybrid reliability analysis method for systems with random and non-parameterized p-boxes based on weight coefficients

This paper establishes a hybrid variable system failure probability optimization model based on sampling methods and weighting coefficients. By introducing auxiliary input variables, important sampling functions, and p-box, failure samples are mapped from the random variable space to the p-box variable space. The new weight coefficients are constructed, including important sampling weights and interval weights. Combining discretization methods and Monte Carlo simulation (MCS), the interval weights are transformed into variables, and constraints conforming to the p-box variable distribution are constructed. After calculating the weighting coefficients for all failure samples, the new failure probability optimization model is built. This model is independent of the performance functions and does not involve cyclic optimization, with computational complexity only related to the dimensions. Six cases are used for method comparison, validating that the new method exhibits higher efficiency and accuracy.

Synchronization of Coupled Neural Networks With Constant Time-Delay Using Sampled-Data Information.

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.

Application of digital transformation in real-time bridge monitoring systems: a case study with GNSS, accelerometer and IoT solution

Abstract Digital transformation is a term that has recently become more popular in all industries such as economics, financial fields, and engineering. The development of the Internet of Things, smart sensor systems, and cloud computing is the basis for forming effective solutions in construction monitoring from cyclic monitoring and post-processing to real-time monitoring and processing; from physical storage systems to virtual systems, and connecting every component in the project. This article presents a study and application of advanced devices including GNSS, acceleration sensors and IoT solutions in bridge construction monitoring. The research aims to apply smart devices and IoT to build real-time monitoring modules for bridge projects following the digital transformation trend. The study uses a GNSS N3 Comnav receiver with a sampling interval of 1Hz, an MPU 6050 acceleration sensor with a sampling frequency of 250 Hz, and an ESP Wi-Fi module. The result of the research is a real-time wireless monitoring system that operates effectively and economically and initially processes data with GNSS and high-frequency acceleration sensors.

MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction

Air quality spatiotemporal prediction can provide technical support for environmental governance and sustainable city development. As a classic multi-source spatiotemporal data, effective multi-source information fusion is key to achieving accurate air quality predictions. However, due to not fully fusing two pieces of information, classical deep learning models struggle to achieve satisfactory prediction results: (1) Multi-granularity: each air monitoring station collects air quality data at different sampling intervals, which show distinct time series patterns. (2) Spatiotemporal correlation: due to human activities and atmospheric diffusion, there exist correlations between air quality data from different air monitoring stations, necessitating the consideration of other air monitoring stations' influences when modeling each air quality time series. In this study, to achieve satisfactory prediction results, we propose the Multi-Granularity Spatiotemporal Fusion Transformer, comprised of the residual de-redundant block, spatiotemporal attention block, and dynamic fusion block. Specifically, the residual de-redundant block eliminates information redundancy between data with different granularities and prevents the model from being misled by redundant information. The spatiotemporal attention block captures the spatiotemporal correlation of air quality data and facilitates prediction modeling. The dynamic fusion block evaluates the importance of data with different granularities and integrates the prediction results. Experimental results demonstrate that the proposed model surpasses 11 baselines by 5% in performance on three real-world datasets.

Using macrobenthic community structure and biotic indices to assess the ecological status of Wulan Estuary, Demak, Indonesia

Abstract. Hartati R, Widianingsih W, Zainuri M, Wardianto Y. 2024. Using macrobenthic community structure and biotic indices to assess the ecological status of Wulan Estuary, Demak, Indonesia. Biodiversitas 25: 3073-3087. Estuaries provide critical habitats for species, and they are valued commercially, recreationally, and culturally. They also filter for sediments and pollutants from rivers before entering the ocean, providing cleaner waters for marine biota. This study presents a unique approach to assessing the ecological status of an estuary using microbenthic community structure and biotic indices with a case study of Wulan Estuary, Demak District, Central Java Province, Indonesia. AZTI’s Marine Biotic Index (AMBI) community structure and biotic indices were used to assess ecological quality at eleven stations and determine the marine macrobenthic fauna's sensitivity to disturbances in two periods, i.e. August 2021 and 2022. A distinct spatial and temporal variation was evident in the macrobenthic community structure, where higher species richness and density were observed in 2022, with some species being more dominant in 2022. The trophic structure analysis showed the dominance of deposit-feeding bivalves in the fine sediments during those sampling intervals. These trophic groups, however, were dispersed less uniformly in the sandy substratum. Compared to the previous year, the density of herbivorous grazers and suspension feeders was higher in 2022. It is also shown by AMBI values that Wulan Estuary belonged to the good (slightly disturbed) to the high (undisturbed) condition, along with other biotic indices, which showed an enhanced habitat quality for benthic organisms. This result revealed the comparative ecological quality of related estuary ecosystems in Indonesia.

System error analysis and target localization of distributed pulse radars

AbstractTarget localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars.

Finite-time H∞ synchronization of semi-Markov jump neural networks with two delay components with stochastic sampled-data control

This article investigates the finite-time H∞ synchronization for semi-Markov jump neural networks with two delay components based on stochastic sampled data control. Additionally, the parametric uncertainties are randomly varying which follows the Bernoulli distributed sequences. In the stochastic sampled data control, the sampling interval ′m′ is supposed to be two different values in the time-varying component with given probability conditions. By constructing triple and quadruple integral term in the Lyapunov-Krasovskii functional (LKF) a new integral inequality technique is addressed to derive the main results. Dissimilar from previous literature, involving the new integral inequality, a delay dependent finite-time H∞ synchronization requirements are acquired with regard to linear matrix inequalities (LMIs). In the end, the effectiveness of the considered stochastic sampled data control finite time synchronization scheme is highlighted by numerical examples.

Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages for feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable capacity estimation approach utilising short-duration random charging voltages during the constant-current charging stage and leveraging convolutional neural networks (CNNs). Based on the user-friendly “Vstart−tend” strategy, two health features including charging voltage and its increment are firstly extracted from random charging segments. Secondly, a feature evolution pattern analysis over the battery's lifespan is proposed to divide the charging voltage range for robust model development. An optimal combination of both the sampling interval and data length is determined for the feature extraction. Then, a two-dimensional CNN model is developed to effectively learn ageing-related knowledge from various random charging segments in a specific charging voltage range. The effectiveness of the proposed approach is ultimately verified using two distinct types of batteries across three operational temperatures. The results demonstrate that the proposed approach show much potential as a promising capacity estimation technique utilising a 600 s random charging segment sampled at a 20 s interval.

Assessing the economic-statistical performance of an attribute SVSSI-np control chart based on genetic algorithms

The SVSSI scheme, featuring three variable sample sizes and two variable sampling intervals, considerably enhances the performance of control charts in detecting shifts. As a novel contribution, this study proposes an optimized design of the SVSSI-np control chart using genetic algorithms, illustrating its superior performance over other sampling schemes by considering economic and statistical perspectives in a unified model. The outcomes of implementing the SVSSI alongside other sampling schemes are assessed via a numerical example, demonstrating the influence of varying parameters such as process failure rate, shift value, and proportion of nonconforming items. Based on the obtained results, the SVSSI scheme outperforms other schemes by achieving over a 10% reduction in costs and over a 30% improvement in reducing time to signal and false alarm rates in most runs. The impact of inaccurately estimating the shift size is also investigated, revealing that incorrect estimates can lead to ineffective adjustments and improvements. Finally, optimal SVSSI designs are explored through comprehensive sensitivity analysis and robust optimization to address uncertainty, and the results of applying fractional factorial design are statistically analyzed. In summary, the SVSSI-np control chart demonstrates potential for enhanced cost reduction, process monitoring, and efficiency gains.

SOIL MICRONUTRIENT CONTENT ANALYSIS AND MAPPING OF AGRICULTURAL SALT-AFFECTED SOILS AROUND ABAYA AND CHAMO LAKES, SOUTH ETHIOPIA RIFT VALLEY

&lt;p&gt;&lt;strong&gt;&lt;span lang="EN-US"&gt;Background.&lt;/span&gt;&lt;/strong&gt;&lt;span lang="EN-US"&gt; Salt-affected soils cause a significant portion of land to become unproductive yearly; its impact is severe in sub-Saharan African nations, especially the arid and semiarid lowlands, and the Rift Valley regions of Ethiopia are typically host to naturally salt-affected areas. &lt;strong&gt;Objective.&lt;/strong&gt; To analyze the micronutrient content and map the micronutrient fertility status of agricultural salt-affected soils around Abaya and Chamo Lakes South Ethiopia Rift Valley. &lt;strong&gt;Methodology.&lt;/strong&gt; A systematic sampling technique was employed to obtain 300 soil samples for the investigation from two depths (0-20 and 20-40cm), with a 600m sampling interval, of which 30 were used. The research data was analyzed using the application of standardized analytical procedures for soil data and descriptive and geostatistical techniques. &lt;strong&gt;Results.&lt;/strong&gt; According to the study, there is low zinc available in the soil but an ample amount of iron, manganese, and copper. In addition, the study's findings revealed that, whereas the remaining micronutrient regional variability is found at long distances, available iron exhibited a regional variation in soil quality at small distances. &lt;strong&gt;Implications.&lt;/strong&gt; The study suggests applying organic matter for better soil structure, water retention, and nutrient availability.&lt;/span&gt;&lt;span lang="EN-US"&gt;Moreover, the results recommend that soils affected by salt might recover using various materials. &lt;strong&gt;Conclusions.&lt;/strong&gt; In the study areas, zinc fertilizer may still be needed for an optimal yield even though the research area has low amounts of zinc fertilizer. The study concluded with management recommendations to minimize the adverse effects of very high micronutrient content on human health and plant growth.&lt;/span&gt;&lt;/p&gt;

Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model

Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming. The proposed RF model showed an excellent prediction accuracy when it was first validated in predicting the effluent conductivity for both CDI (R2 = 0.86) and BDI (R2 = 0.95) data. Moreover, the RF model successfully predicted the concentration of each ion (Na⁺, K⁺, Ca2⁺, and Cl⁻) from the conductivity values. The accuracy of the ion concentration prediction was even higher than that of the effluent conductivity prediction, likely owing to the linear correlation between the input and output variables of the dataset. The effect of the sampling interval was also evaluated for conductivity and ion concentrations, and there was no significant difference up to sampling intervals of <80 s based on the error value of the model. These findings suggest that an RF model can be used to predict ion concentrations in CDI/BDI, which may be used as core indicators in evaluating desalination performance.

Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions.

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications.

Large sampling intervals for learning and predicting chaotic systems with reservoir computing

Reservoir computing (RC) is an efficient artificial neural network for model-free prediction and analysis of dynamical systems time series. As a data-based method, the capacity of RC is strongly affected by the time sampling interval of training data. In this paper, taking Lorenz system as an example, we explore the influence of this sampling interval on the performance of RC in predicting chaotic sequences. When the sampling interval increases, the prediction capacity of RC is first enhanced then weakened, presenting a bell-shaped curve. By slightly revising the calculation method of the output matrix, the prediction performance of RC with small sampling interval can be improved. Furthermore, RC can learn and reproduce the state of chaotic system with a large time interval, which is almost five times larger than that of the classic fourth-order Runge–Kutta method. Our results show the capacity of RC in the applications where the time sampling intervals are constrained and laid the foundation for building a fast algorithm with larger time iteration steps.