Here two parameter identification methods are proposed : 1 ) based on the principle of the orthogonality among filter residuals , a parameter identification method is proposed , which results in parameter - identification based filter ( pibf ) . 2 ) based on multiple model idea , the parameter is quantified in its value range and then adaptively identified via model switches 本文使用两种方法进行参数辨识： 1 )从滤波残差正交性原理出发，推导出参数自适应辨识滤波器? pibf ； 2 )根据多模型的思想，对未知参数在参数空间进行量化，进而通过交互式多模型算法( imm )的模型切换实现参数的在线辨识。
Then , 8 - error module is established , and the osl ( open + short + load ) method and the slt method which are standard calibration ones and the tls ( thru + line + short ) method which is nonstandard calibration one are obtained on the basis of the error module . good measurement results can be acquired by using nonstandard calibration technology that is proved by experiment . secondly , oddity point maybe appear in the measurement results after network analyzer is corrected by nonstandard calibration technology 用十二项误差模型和八项误差模型的非标准校正方法对测量系统进行误差校正后,测量结果一般都会出现奇异点问题,且奇异点一般出现在传输线传输参数x相位的过0点,这可能与这些方法都使用了未知参数的传输线有关。
Firstly , the inversion of the ground motion with the unknown parameters and limited measurements was studied by the sub - structural identification , and the least - square algorithm of unknown input was built . secondly , based on the estimated input , structural parameter down to the element level was identified in time domain by extended kalman filter algorithm . several cases of a 6 - story frame s 首先，使用子结构识别技术，研究了未知参数、观测不完整条件下的地震动输入反演问题，建立了未知输入的最小二乘估计算法；其次，在估计地震动输入的基础上，使用广义kalman滤波器方法，识别全部单元结构参数。
In this paper , we define the test contradiction in hypothesis test and point out that it will result in wrong judgment with quite large probability if neglecting the contradiction but only by conventional test when testing unilaterally parameter of distribution function especially in engineering , economic and scientific research , it ' s quite possible to make such a mistake as will lead to serious consequences 摘要定义了假设检验中的“检验矛盾”概念，指出对分布未知参数进行单边检验过程中，仅仅依靠常规形式检验而忽视“检验矛盾” ，将以相当大的概率导致错误的判断，特别在工程、经济、科学研究等项目中作出类似错误的判断，将产生严重的危害。
Even with unmodeled dynamics and bounded disturbances presented the controller has good robust performance without need of correct dynamic model and parameter estimation . the designed controller has applied to a model of three - generators hvdc system , and the simulations are performed with netomac 并且这种控制器的设计不需要有精确的系统动态模型，不需要未知参数来满足线性依赖条件，不需要具体的系统参数估计，即使存在未建模动态和有界扰动，系统仍然具有很好的鲁棒性。
In order to do this , a chaotic system is designed , in which , one of the unknown parameters is the measured value or parameter being estimated . between the parameter and the symbolic series corresponding to chaotic orbits of the system output , there should be a homologous relationship 基本思想是设法构造一个混沌系统，将被测量或待估的参数作为系统中的某一未知参数，并在该参数与系统输出的混沌轨道对应的符号序列之间建立起一一对应关系，用符号空间的距离测度来表示参数的变化。
Abstract : a group of phase shifting algorithms is derived by the method of undeterminedcoefficients ( ucm ) . the unknown coefficients are set in a general algorithm expression , and then solved by adding bonding equations according to error compensation requirements . thus the algorithm design and error alalysis are carried out simultaneously . phase shifting algorithms derived by this method are insensitive to one or more error sources which are considered in algorithm design 文摘:提出了一种以误差补偿为目的的新的相移算法设计方法?待定系数法，该方法首先在算法表达式中引入一系列待定参数，根据对误差补偿的要求提供约束方程，从而解出这些未知参数.待定系数法使得算法设计和误差分析是同时进行的，改变了以往先设计算法再进行误差分析和评价的过程，由待定系数法导出的相移算法对算法设计中所考虑的误差源具有良好的补偿性能
This paper studies a design method of decentralized signal detection system which consists of adaptive fuzzied local - detectors and a data fusion rule of on - line self - learning weights . the local - detectors for inaccurate signal parameters are modeled by means of fuzzy sets which can be adapted to change of the inaccurate signal parameteres . the data fusion center where the optimal declsion rules are used as objective function can learn the local decision weights on - line . the robustness of the fuzzied local - detectors and the adaptability of the self - learned fusion rule make it true that the detection performance of the decentralized detection system is improved under uncertainty and this system can also process the decentralized signal detection with a unknown parameter of unknown distribution or non - random unknown parameter 本文研究了一种由局部自适应模糊检测器和在线自学习融合算法所构成的分布式信号检测系统的设计方法.由模糊集对不精确信号参数的局部检测器进行建模，该模糊模型可自适应不精确信号参数的变化.融合中心以最佳融合规则作为目标函数在线自学习局部判决的权重.局部模糊检测器的鲁棒性和自学习融合算法的自适应性使该分布式检测系统在不确定环境下的检测性能得到提高.也使该系统能够处理未知分布的未知参数以及非随机未知参数的分布式信号检测
So we mainly do the following works : firstly , we restrict the population distribution in the family of exponential distributions , construct the program of sequential confidence intervals for the one - dimensioned function of unknown parameters with general form , and acquire its some asymptotic properties , such as asymptotic consistency , asymptotic efficiency and bounded cost of ignorance 为此，我们做了以下工作：我们首先把总体分布局限在指数型分布族中，建立了未知参数一般形式的一维函数的序贯置信区间程序，并得出它的渐近相合性、渐近有效性和未知代价的有界性。
