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Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method 认领 引用 被引量：8
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作者 Faming Huang Zuokui Teng +4 位作者 Chi Yao Shui-Hua Jiang Filippo Catani Wei Chen Jinsong Huang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第1期213-230,共18页
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a... In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors. 展开更多
关键词 Landslide susceptibility prediction Conditioning factor errors Low-pass filter method Machine learning models Interpretability analysis
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Analogue correction method of errors and its application to numerical weather prediction 认领 引用 被引量：10
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作者 高丽 任宏利 +1 位作者 李建平 丑纪范 《Chinese Physics B》 CAS 2006年第4期882-889,共8页
In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff... In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model. 展开更多
关键词 numerical weather prediction analogue correction method of errors reference state,analogue-dynamical model
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A novel method to predict static transmission error for spur gear pair based on accuracy grade 认领 引用 被引量：5
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作者 LIU Chang SHI Wan-kai +1 位作者 Francesca Maria CURÀ Andrea MURA 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第11期3334-3349,共16页
This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modif... This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modifications(PMs)are considered.For the prediction,a discrete gear model for generating the error tooth profile based on the ISO accuracy grade is presented.Then,the gear model and a tooth deflection model for calculating the tooth compliance on gear meshing are coupled with the transmission error model to make the prediction by checking the interference status between gear and pinion.The prediction method is validated by comparison with the experimental results from the literature,and a set of cases are simulated to study the effects of MEs,AEs,TDs and PMs on the static transmission error.In addition,the time-varying backlash caused by both MEs and AEs,and the contact ratio under load conditions are also investigated.The results show that the novel method can effectively predict the range of the static transmission error under different accuracy grades.The prediction results can provide references for the selection of gear design parameters and the optimization of transmission performance in the design stage of gear systems. 展开更多
关键词 gear transmission error time-varying backlash prediction method accuracy grade
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Gain adaptive tuning method for fiber Raman amplifier based on two-stage neural networks and double weights updates 认领 引用
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作者 MU Kuanlin WU Yue 《Optoelectronics Letters》 EI 2025年第5期284-289,共6页
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph... We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy. 展开更多
关键词 gain adaptive tuning connection weights error predicted target gains training connection weights unified nn gain adaptive tuning method double weights updates fiber raman amplifier fra
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Time-varying reliability analysis of a reservoir bank slope considering creep behavior and sequential Bayesian updating 认领 引用
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作者 Wenyu Zhuang Qingchao Lyu +4 位作者 Yaoru Liu Kai Zhang Ting Liu Junlei Bai Qun Zhang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2104-2121,共18页
The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of m... The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of mechanical parameters.In this study,a novel time-varying reliability analysis framework based on sequential Bayesian updating of mechanical parameters is proposed.The inverse parameters account for damage time-dependent behavior,incorporating water effect and a strain-driven softening-hardening process that depends on sliding states.The likelihood function is enhanced to simultaneously consider observation error,surrogate model prediction error,and model structural error,with the introduction of physical penalty.Exploration of the high-dimensional parameter space is achieved via the Hamiltonian Monte Carlo(HMC)method and the physics knowledge-based time-dependent deformation surrogate model.The time-varying reliability analysis of the slope is performed using the multi-grid method.Taking a reservoir bank slope as a case study,the sequential updating of 12 mechanical parameters is conducted based on deformation time series from 16 monitoring points,thereby validating the proposed framework.The results indicate that the proposed framework effectively captures the posterior distribution of mechanical parameters,with the case slope remaining in a critically stable state after overall sliding,showing a high failure probability.Introducing model structural error can reduce parameter compensation,and a reasonable sequential updating step size can improve inversion accuracy. 展开更多
关键词 Sequential Bayesian updating Probabilistic back analysis Time-varying reliability Reservoir bank slope Model structural error Surrogate model prediction error Markov chain Monte Carlo(MCMC)method Multi-grid method
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A COMBINED VERIFICATION METHOD FOR PREDICTABILITY OF PERSISTENT HEAVY RAINFALL EVENTS OVER EAST ASIA BASED ON ENSEMBLE FORECAST 认领 引用 被引量：3
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作者 WU Zhi-peng CHEN Jing +2 位作者 ZHANG Han-bin CHEN Fa-jing ZHUANG Xiao-ran 《Journal of Tropical Meteorology》 SCIE 2020年第1期35-46,共12页
Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from C... Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of the metrics of ICP,the predictability of two typical PHR events in June 2010 and June 2011 is estimated.The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research. 展开更多
关键词 persistent heavy rainfall verification method predictability ensemble prediction error analysis
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基于PEM-ABC算法的小型无人直升机系统辨识 认领 引用 被引量：5
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作者 丁力 吴洪涛 +2 位作者 姚裕 申浩宇 李晓芳 《农业机械学报》 EI CAS CSCD 北大核心 2016年第1期8-14,共7页
针对小型无人直升机在悬停状态下飞行动力学模型的系统辨识问题,提出了一种基于预测误差法与人工蜂群算法(PEM-ABC)结合的辨识算法。该算法将系统辨识问题转化为优化问题,用PEM算法确定搜索空间的范围;雇佣蜂搜索阶段采用改进的自适应... 针对小型无人直升机在悬停状态下飞行动力学模型的系统辨识问题,提出了一种基于预测误差法与人工蜂群算法(PEM-ABC)结合的辨识算法。该算法将系统辨识问题转化为优化问题,用PEM算法确定搜索空间的范围;雇佣蜂搜索阶段采用改进的自适应搜索策略加快收敛速度;跟随蜂搜索阶段引入一种新的概率选择方式保证种群多样性;侦察蜂搜索阶段利用混沌算子来提高全局搜索能力。通过机载设备采集到的飞行实验数据,对辨识获得的模型进行了分析与验证。结果表明:采用该辨识方法,估计出了无人直升机动力学模型的未知参数,与PEM算法和传统人工蜂群算法相比,所提算法的辨识精度更高,具有重要的工程使用价值。 展开更多
关键词 小型无人直升机 系统辨识 预测误差法 人工蜂群算法
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使用预测误差方法的助听器凸组合比例声反馈消除算法 认领 引用
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作者 王森童 全智 《应用声学》 CSCD 北大核心 2026年第1期245-259,共15页
传统的自适应声学反馈消除算法在兼顾收敛速度和稳态性能之间存在困难,而输入信号与反馈信号之间的高相关性进一步限制了算法性能。针对这些问题,提出了使用预测误差方法的凸组合比例算法。该算法结合两个不同步长的自适应滤波器,并引... 传统的自适应声学反馈消除算法在兼顾收敛速度和稳态性能之间存在困难,而输入信号与反馈信号之间的高相关性进一步限制了算法性能。针对这些问题,提出了使用预测误差方法的凸组合比例算法。该算法结合两个不同步长的自适应滤波器,并引入比例机制和预测误差方法以加速初始收敛和增强跟踪能力,消除了信号之间的高相关性。仿真结果显示,与传统方法相比,所提算法在处理声学信号时,显著降低了失调量并提高了额外稳态增益。 展开更多
关键词 回声消除算法 自适应滤波器 凸组合 预测误差方法 比例自适应滤波法
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PMSLM-MPCC系统逆变器非线性补偿策略 认领 引用
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作者 张慧 缪仲翠 +1 位作者 董利元 丰玉鑫 《南开大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第1期9-16,共8页
模型预测电流控制因其独特的优越性,已广泛应用于永磁同步直线电机驱动系统.然而,由于逆变器存在死区时间、功率器件开通/关断延时及开关管压降等非线性因素,系统输出电压容易发生畸变,从而影响模型预测电流控制的性能.为解决这一问题,... 模型预测电流控制因其独特的优越性,已广泛应用于永磁同步直线电机驱动系统.然而,由于逆变器存在死区时间、功率器件开通/关断延时及开关管压降等非线性因素,系统输出电压容易发生畸变,从而影响模型预测电流控制的性能.为解决这一问题,提出了一种结合直轴注入电流法与拉格朗日插值法的误差电压计算方法.该方法首先通过直轴注入电流法建立误差电压与电流幅值之间的映射关系,并利用拉格朗日插值法精确获得误差电压,从而实现逆变器非线性误差电压的前馈补偿.仿真结果表明,所提方法能够有效地进行暂态和稳态补偿,显著改善电流波形,抑制由于逆变器非线性效应引起的电流谐波畸变. 展开更多
关键词 永磁同步直线电机 模型预测电流控制 误差电压补偿 拉格朗日插值法
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含缺口的复合材料层合板疲劳寿命预测 认领 引用
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作者 蒋寒斌 郭俊华 +1 位作者 童宗鹏 温华兵 《机械制造与自动化》 2026年第4期44-48,共5页
缺口结构的疲劳寿命预测,需要考虑缺口附近存在的应力梯度影响。如直接使用金属材料上广泛应用的应力场强法预测含缺口的复合材料层合板的疲劳寿命,误差会很大。提出二维平面矢量场强法(ETEVM)和改进的三维空间矢量场强法(CTSVM'),... 缺口结构的疲劳寿命预测,需要考虑缺口附近存在的应力梯度影响。如直接使用金属材料上广泛应用的应力场强法预测含缺口的复合材料层合板的疲劳寿命,误差会很大。提出二维平面矢量场强法(ETEVM)和改进的三维空间矢量场强法(CTSVM'),预估疲劳强度和疲劳寿命,经仿真验证预估精度均比经典一维应力场强法(COSFI)高。其中CTSVM'预测误差在两倍误差带之间,由此证明改进的方法可以用于疲劳寿命预测。 展开更多
关键词 复合材料层合板 应力梯度 应力场强法 两倍误差带 疲劳寿命预测
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Subsystem model-based close-loop grey-box identification method for hydraulic stewart platform 认领 引用
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作者 唐建林 董彦良 赵克定 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第2期107-112,共6页
In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification m... In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification method was put forward when consider the main coupling effects of hydraulic Stewart platform.Firstly,the whole system is divided into three TITO(Two Input Two Output) subsystems according to the characteristics of the pseudo-mass matrix,hence transfer function matrix model of the subsystem can also be found.Secondly,since the Stewart platform is unstable,the close-loop transfer model of the subsystem is derived under the proportional controllers.The inverse M serial is adopted as the identification signal to get the experimental data.All parameters of the subsystem are determined in close-loop indirect identification by PEM(Prediction Error Method).Finally,a case study validates the correctness and effectiveness of the subsystem model-based close-loop grey-box identification method for hydraulic Stewart platform. 展开更多
关键词 hydraulic Stewart platform pseudo-mass matrix prediction error method close-loop indirect identification grey-box model
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基于PEM法和GA的无人直升机模型辨识 认领 引用
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作者 严军辉 贾秋玲 《电子设计工程》 2013年第17期83-85,88,共3页
无人直升机的数学模型是设计先进控制系统的基础,首先采用机理建模的方法分析了直升机的飞行力学特性,加入旋翼运动,并得到了参数化状态空间模型。辨识之前对实验数据进行野值识别,剔除,补正,滤波,去趋势项等处理,利用预报误差法进行系... 无人直升机的数学模型是设计先进控制系统的基础,首先采用机理建模的方法分析了直升机的飞行力学特性,加入旋翼运动,并得到了参数化状态空间模型。辨识之前对实验数据进行野值识别,剔除,补正,滤波,去趋势项等处理,利用预报误差法进行系统辨识,再运用遗传算法对预报误差辨识结果进行优化,验证结果表明,达到辨识和优化的目的。 展开更多
关键词 无人直升机 系统辨识 预报误差法 遗传算法
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Redesigned Surface Based Machining Strategy and Method in Peripheral Milling of Thin-walled Parts 认领 引用 被引量：7
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作者 JIA Zhenyuan GUO Qiang +1 位作者 SUN Yuwen GUO Dongming 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2010年第3期282-287,共6页
Currently,simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge.Existing compensating methods are mainly focusing on 3-aixs... Currently,simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge.Existing compensating methods are mainly focusing on 3-aixs machining,which sometimes only take one given point as the compensative point at each given cutter location.This paper presents a redesigned surface based machining strategy for peripheral milling of thin-walled parts.Based on an improved cutting force/heat model and finite element method(FEM)simulation environment,a deflection error prediction model,which takes sequence of cutter contact lines as compensation targets,is established.And an iterative algorithm is presented to determine feasible cutter axis positions.The final redesigned surface is subsequently generated by skinning all discrete cutter axis vectors after compensating by using the proposed algorithm.The proposed machining strategy incorporates the thermo-mechanical coupled effect in deflection prediction,and is also validated with flank milling experiment by using five-axis machine tool.At the same time,the deformation error is detected by using three-coordinate measuring machine.Error prediction values and experimental results indicate that they have a good consistency and the proposed approach is able to significantly reduce the dimension error under the same machining conditions compared with conventional methods.The proposed machining strategy has potential in high-efficiency precision machining of thin-walled parts. 展开更多
关键词 redesigned surface tool path part deflection error prediction finite element method
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基于长短期记忆网络的电网中长期负荷预测方法 认领 引用
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作者 黄高潮 《自动化应用》 2026年第12期226-228,232,共3页
在电网负荷预测过程中,若直接将历史突变数据作为输入,则所构建的预测模型会产生拟合误差,进而导致负荷预测相对误差较大,影响电网的后续规划。因此,设计基于长短期记忆网络(LSTM)的电网中长期负荷预测方法。应用偏最小二乘回归方法,从... 在电网负荷预测过程中,若直接将历史突变数据作为输入,则所构建的预测模型会产生拟合误差,进而导致负荷预测相对误差较大,影响电网的后续规划。因此,设计基于长短期记忆网络(LSTM)的电网中长期负荷预测方法。应用偏最小二乘回归方法,从历史突变数据中提取异常主成分,获得电网中长期异常突变负荷数据。引入LSTM的遗忘门、输入门以及输出门,获取输出负荷状态信息,构建负荷数据预测模型。将设定的最优拟合目标当作负荷需求,对电网中长期负荷预测模型的拟合误差进行修正,实现负荷的精准预测。实验结果显示,电网A区、B区、C区的负荷预测相对误差在0%~0.10%的范围内变化,预测精度较高,对于电网的后续规划具有重要作用。 展开更多
关键词 长短期记忆网络 电网 中长期负荷 预测方法 相对误差
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基于PEM的三自由度直升机模型辨识 认领 引用 被引量：3
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作者 李亚帅 邵宗凯 《传感器与微系统》 CSCD 2017年第6期49-52,56,共4页
针对三自由度(3-DOF)直升机平台的特点,提出了一种基于预测误差法(PEM)的模型频域辨识方法,建立了机理模型,运用扫频技术得到巡航飞行状态直升机3个通道的输入—输出数据;分析了偏相干函数和复合窗函数,通过PEM进行了模型的频域辨识,得... 针对三自由度(3-DOF)直升机平台的特点,提出了一种基于预测误差法(PEM)的模型频域辨识方法,建立了机理模型,运用扫频技术得到巡航飞行状态直升机3个通道的输入—输出数据;分析了偏相干函数和复合窗函数,通过PEM进行了模型的频域辨识,得到了状态空间方程的待辨识参数和直升机的参数化模型。通过时域飞行和模型预测响应的对比,验证了该模型的准确性和该辨识方法的有效性。 展开更多
关键词 三自由度直升机 预测误差法 频域辨识 状态空间方程
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基于子空间和PEM的无人直升机两阶段参数辨识 认领 引用 被引量：4
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作者 邵明强 李广文 徐恒 《飞行力学》 CSCD 北大核心 2013年第4期367-371,共5页
在研究子空间辨识方法和预测误差方法 (PEM)的基础上,提出了一种两阶段辨识方法,研究无人直升机的参数辨识问题。首先采用子空间方法得到初始参数模型,然后通过PEM方法得到参数化的直升机模型。为验证方法的有效性,以某型无人直升机实... 在研究子空间辨识方法和预测误差方法 (PEM)的基础上,提出了一种两阶段辨识方法,研究无人直升机的参数辨识问题。首先采用子空间方法得到初始参数模型,然后通过PEM方法得到参数化的直升机模型。为验证方法的有效性,以某型无人直升机实测数据为例进行参数辨识,结果表明该方法有良好的辨识精度。 展开更多
关键词 无人直升机 子空间 预测误差法 参数辨识
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Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems 认领 引用
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作者 LU Fei SUN Ruishan +2 位作者 CHEN Zichen CHEN Huiyu WANG Xiaomin 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第2期316-324,共9页
The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on expe... The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year. 展开更多
关键词 small sample and poor information prediction method performance optimization method performance combined prediction error elimination optimization model Markov optimization
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Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model 认领 引用
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作者 Hynek Bednár Ales Raidl Jiri Miksovský 《International Journal of Automation and computing》 CSCD 2014年第3期256-264,共9页
The growth of small errors in weather prediction is exponential on average.As an error becomes larger,its growth slows down and then stops with the magnitude of the error saturating at about the average distance betwe... The growth of small errors in weather prediction is exponential on average.As an error becomes larger,its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly.This paper studies the error growth in a low-dimensional atmospheric model before,during and after the initial exponential divergence occurs.We test cubic,quartic and logarithmic hypotheses by ensemble prediction method.Furthermore,the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method.The study shows that a small error growth is best modeled by the quadratic hypothesis.After the error exceeds about a half of the average value of variables,logarithmic approximation becomes superior.It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent.We conclude that the size of the error at the least upper bound(supremum)of time length is equal to 1 and it is invariant to these variables.Predictability,as a time interval,where the model error is growing,is for small initial error,the sum of the least upper bound of time interval of exponential growth and predictability for the size of initial error equal to 1. 展开更多
关键词 Chaos planetary atmospheres prediction methods error analysis modeling.
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基于PEM的辅助动力装置系统辨识与仿真 认领 引用 被引量：4
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作者 江群 王道波 李猛 《燃气涡轮试验与研究》 2010年第3期46-48,62,共3页
辅助动力装置(APU)是一个复杂的非线性系统,为了研究APU的工作特性,必须对其进行数学建模。本文依据某型APU地面试车数据,采用基于预测误差法(PEM)的输出误差模型进行系统辨识,建立了APU某一稳态点的"小偏差"数学模型,以满足... 辅助动力装置(APU)是一个复杂的非线性系统,为了研究APU的工作特性,必须对其进行数学建模。本文依据某型APU地面试车数据,采用基于预测误差法(PEM)的输出误差模型进行系统辨识,建立了APU某一稳态点的"小偏差"数学模型,以满足后续控制规律的设计和研究。MATLAB仿真结果表明,此方法对APU模型辨识可行。验模表明,所建模型精度很高,且能实时准确反映APU性能,因而可在该状态下基于此模型进行控制器设计。 展开更多
关键词 辅助动力装置 预测误差方法 系统辨识 数学模型 仿真
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An Evaluation of Human Error Probabilities for Critical Failures in Auxiliary Systems of Marine Diesel Engines 认领 引用
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作者 Hakan Demirel 《Journal of Marine Science and Application》 CSCD 2021年第1期128-137,共10页
Human error,an important factor,may lead to serious results in various operational fields.The human factor plays a critical role in the risks and hazards of the maritime industry.A ship can achieve safe navigation whe... Human error,an important factor,may lead to serious results in various operational fields.The human factor plays a critical role in the risks and hazards of the maritime industry.A ship can achieve safe navigation when all operations in the engine room are conducted vigilantly.This paper presents a systematic evaluation of 20 failures in auxiliary systems of marine diesel engines that may be caused by human error.The Cognitive Reliability Error Analysis Method(CREAM)is used to determine the potentiality of human errors in the failures implied thanks to the answers of experts.Using this method,the probabilities of human error on failures were evaluated and the critical ones were emphasized.The measures to be taken for these results will make significant contributions not only to the seafarers but also to the ship owners. 展开更多
关键词 Marine diesel engine Human error prediction Cognitive Reliability Error Analysis Method Critical failures Marine engineering
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