共查询到19条相似文献,搜索用时 125 毫秒
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提出了一种适合木材干燥过程建模的多模型数据融合算法,通过该方法构建了数据融合模型。分别用BP神经网络和动态递归网络建立了木材干燥基准模型,利用自适应加权算法对两模型输出进行融合,通过实验干燥数据仿真表明:融合后的木材含水率预测值的方差为0.125 3,高于任何一个单独模型的预测精度。 相似文献
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木材干燥是一个复杂的非线性系统,由于木材结构复杂且具有多样性和变异性,很难建立一个理想的符合木材干燥过程的数学模型。利用遗传算法的全局寻优能力优化BP神经网络连接权值系数,分别用BP和GA-BP两种算法建立了木材干燥基准模型。对比结果表明:GA-BP算法建立木材干燥基准模型提高了期望误差精度和收敛速度,避免了BP算法陷入局部极小值,预测平均误差为1.0413%,具有较好的预测精度。 相似文献
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木材干燥是一个复杂的非线性系统,由于木材结构复杂且具有多样性和变异性,很难建立一个理想的符合木材干燥过程的数学模型。利用遗传算法的全局寻优能力优化BP神经网络连接权值系数,分别用BP和GA—BP两种算法建立了木材干燥基准模型。对比结果表明:GA—BP算法建立木材干燥基准模型提高了期望误差精度和收敛速度,避免了BP算法陷入局部极小值.预测平均误差为1.0413%,具有较好的预测精度。 相似文献
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针对木材干燥系统具有非线性、强耦合的特性,难以建立准确的数学模型,提出一种基于小波神经网络的建模方法。通过木材干燥窑内木材含水率传感器、温度传感器和湿度传感器采集的数据建立小波神经网络模型,并通过模型预测木材含水率传感器的测量值。小波神经网络将BP神经网络在非线性问题上自学习的能力与小波表征信号局部信息的能力相结合,具有很强的自适应分辨性和容错能力。利用实际木材干燥过程中采集的数据作为训练样本进行仿真实验。结果表明:小波神经网络方法建立的模型能够预测木材含水率传感器的测量值,模型泛化能力强,预测精度高于BP神经网络建立的模型,验证了小波神经网络对木材干燥窑内传感器建模的可行性和有效性。 相似文献
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论述了木材干燥窑自动测试系统的硬件、软件设计.重点介绍了各个参数测量传感器的工作原理、提高被测参数测量精度的措施,以及测量过程中动态补偿的方法.通过实验验证,该系统运行稳定,测量精度高,数据采集实时可靠.测温精度为±0.1℃.平衡含水率和木材含水率精度为±1%. 相似文献
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针对木材干燥过程中含水率的检测问题,提出了一种基于SAGA优化BP神经网络的木材含水率预测方法,即采用遗传算法与模拟退火算法相结合的学习策略,利用SAGA的全局寻优能力优化BP网络的权值和阈值.仿真试验表明,优化后的BP网络表现出良好的预测性能,缩短了训练时间,避免了BP算法陷入局部小,具有很高的预测精度. 相似文献
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在实际工业过程中,模型参数往往具有一定的时变性和非线性。为了能够有效地实施过程操作优化,常常要对过程模型参数进行在线估计。滚动时域估计方法是解决非线性系统模型参数在线估计的1种实用方法。滚动时域估计方法的关键问题之一是抵达成本(Arrival Cost)的计算,针对简化计算抵达成本带来的精度问题,提出采用无迹卡尔曼滤波(UKF)算法来近似估算目标函数中的抵达成本。最后,将基于UKF的滚动时域估计方法应用于2个例子中。结果表明,基于UKF的滚动时域估计方法具有较好的估计效果。 相似文献
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为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。 相似文献
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C. Guardiola B. Pla D. Blanco-Rodriguez L. Eriksson 《Control Engineering Practice》2013,21(11):1455-1468
Nox estimation in diesel engines is an up-to-date problem but still some issues need to be solved. Raw sensor signals are not fast enough for real-time use while control-oriented models suffer from drift and aging. A control-oriented gray box model based on engine maps and calibrated off-line is used as benchmark model for Nox estimation. Calibration effort is important and engine data-dependent. This motivates the use of adaptive look-up tables. In addition to, look-up tables are often used in automotive control systems and there is a need for systematic methods that can estimate or update them on-line. For that purpose, Kalman filter (KF) based methods are explored as having the interesting property of tracking estimation error in a covariance matrix. Nevertheless, when coping with large systems, the computational burden is high, in terms of time and memory, compromising its implementation in commercial electronic control units. However look-up table estimation has a structure, that is here exploited to develop a memory and computationally efficient approximation to the KF, named Simplified Kalman filter (SKF). Convergence and robustness is evaluated in simulation and compared to both a full KF and a minimal steady-state version, that neglects the variance information. SKF is used for the online calibration of an adaptive model for Nox estimation in dynamic engine cycles. Prediction results are compared with the ones of the benchmark model and of the other methods. Furthermore, actual online estimation of Nox is solved by means of the proposed adaptive structure. Results on dynamic tests with a diesel engine and the computational study demonstrate the feasibility and capabilities of the method for an implementation in engine control units. 相似文献
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机场道面接缝积水会逐渐破坏机场道面平坦度,影响飞行安全.传统的机场接缝除湿方式易导致水泥焦化且效率低下.利用微波辐射技术设计了机场水泥道面接缝积水的检测系统,将含水量的测定转化为对电压的测量.采用最小二乘法和卡尔曼滤波分别对硬件测量误差进行补偿,分析结果表明,使用最小二乘法优化数据可将平均误差率由0.131 1%降为0.019 4%,而运用卡尔曼滤波算法则可降至0.008 1%,能够显著提高机场道面接缝积水含水量的测定精度. 相似文献
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This paper presents a real‐time nonlinear moving horizon observer (MHO) with pre‐estimation and its application to aircraft sensor fault detection and estimation. An MHO determines the state estimates by minimizing the output estimation errors online, considering a finite sequence of current and past measured data and the available system model. To achieve the real‐time implementability of such an online optimization–based observer, 2 particular strategies are adopted. First, a pre‐estimating observer is embedded to compensate for model uncertainties so that the calculation of disturbance estimates in a standard MHO can be avoided without losing much estimation performance. This strategy significantly reduces the online computational complexity. Second, a real‐time iteration scheme is proposed by performing only 1 iteration of sequential quadratic programming with local Gauss‐Newton approximation to the nonlinear optimization problem. Since existing stability analyses of real‐time moving horizon observers cannot address the incorporation of the pre‐estimating observer, a new stability analysis is performed in the presence of bounded disturbances and noises. Using a nonlinear passenger aircraft benchmark simulator, the simulation results show that the proposed approach achieves a good compromise between estimation performance and computational complexity compared with the extended Kalman filtering and 2 other moving horizon observers. 相似文献