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偏差辨识
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  “偏差辨识”译为未确定词的双语例句
     For identification of nonlinear systems using recurrent high order neural networks, a set of available robust stable learning rules and the corresponding structures based on the Lyapunov stability method are presented. It ensures that the identification error and parameters of the neural networks are stable in the sense of uniform ultimate boundedness when a nonlinear system is identified, even in the case there exists modeling error.
     将高阶动态神经网络作为非线性系统的辨识模型 ,运用 Lyapunov稳定性理论 ,提出一种有效的鲁棒稳定学习规则及相应的学习网络结构 ,从而确保在对非线性系统辨识时 ,即使存在建模偏差 ,辨识误差和动态神经网络的参数能一致最终有界 (UUB)稳定 ,解决了动态神经网络的学习稳定性问题 .
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  相似匹配句对
     Detection and Identification of Measurement Biases
     量测偏差的检测与辨识
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     The Misuse of Discrepancy in Experiment Design
     偏差的误用
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     Errors in Y axis
     Y向偏差
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     BIAS COMPENSATED LEAST SQUARES METHOD FOR IDENTIFICATION OF CLOSED LOOP SYSTEMS
     闭环系统辨识偏差最小二乘法
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     Parameter Identification for Mircromotor
     微型电动机的参数辨识
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In order to determine aircraft performance stability and contrdl characteristics from test data, flight stale estimation is needed.Usually, extended, Kalman filter and fixed-interval smoother are used for handling the nonlinear estimation problems of both the state and parameter.However, practical experience and theoretical studies show that this augmented algorithm, in fact, is not satisfactory for parameter estimation.In order to provide a more accurate and practical method for flight state estimation, this...

In order to determine aircraft performance stability and contrdl characteristics from test data, flight stale estimation is needed.Usually, extended, Kalman filter and fixed-interval smoother are used for handling the nonlinear estimation problems of both the state and parameter.However, practical experience and theoretical studies show that this augmented algorithm, in fact, is not satisfactory for parameter estimation.In order to provide a more accurate and practical method for flight state estimation, this paper presents a new separated bias identification and state estimation algorithm which is used for both the flight state estimation and instrumentation errors identification.In order to get high accuracy, U-D factorization methods are applied to improving the computational stability and efficiency. Discrete-time model of aircraft motion used for decreasing modeling errors.Finally, this new approach has been compared with the conventional method by digital simulation and actual flight test data compatibility check.The results calculated show that the new method can give more consistent results than the usual one for a different initial values and the noise statistics.Moreover, this approach can be used for the compatibility check of lower sampled flight test data.

本文根据最小方差估计和分离算法原理,提出一种新的非线性状态估计和偏差辨识的分离算法。并用此算法确定飞行状态和测试仪器的误差,同时U-D分解保证计算效率和数值稳定性。为了得到数据相容性检验的准确结果,本文采用直接离散化的飞机运动模型,以减小模型误差。通过仿真并在我国两种歼击机上实际应用,结果表明本文所给的算法对不同的初值和噪声统计特性都能得到飞行数据相容性检验的一致结果,并能用于低采样率下的数据相容性检验。

A kind of learning algorithm and mean-error model identification method of BP neural network have been presented in this paper. Based on the simulation example of dynamotor cooling model, the method presented in this paper has been tested successfully on improving the simulation precision aspect. ;;;;

提供了一种神经元网络学习算法和平均偏差辨识模型方法,通过发电机冷却模型的仿真实验结果,验证了作者提供的神经元网BPBP络方法能够实现提高仿真精度的目的。

 
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