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将低维空间线性不可分问题转化为高维空间线性可分问题,是自适应线性元件划分线性不可分空间的基本原理,文章探讨了基于这个原理实现划分线性不可分区域的的若干个方法。 相似文献
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将低维空间线性不可分问题转化为高维空间线性可分问题,是自适应线性元件划分线性不可分空间的基本原理,文章探讨了基于这个原理实现划分线性不可分区域的的若干个方法. 相似文献
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以往确定Wang-Li模型参数使用线性回归方法,所求得的模型参数不是精确值。以拟合误差平方和为目标函数,建立了模型参数拟合模型,使用非线性最小二乘法求解约束优化问题,所得到的模型参数是精确值。 相似文献
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在纵向数据的条件下,针对混合系数的线性模型的共线性,给出混合系数的线性模型参数新的有偏的参数估计,证明在纵向数据的条件下该估计相对于最小的二乘估计与岭估计的优势,并对线性模型的参数估计可容许性进行讨论,对线性模型参数进行选择,并给出混合系数的线性模型参数估计的形式,提出新的混合系数的线性模型的局部Stein参数估计。说明,利用该方法能有效地对纵向数据下的混合系数的线性模型的参数进行估计。 相似文献
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针对一般模糊线性回归模型在参数确定方面存在的问题,提出一种改进的参数求解方法,并采用启发式算法及遗传算法（ＧＡｓ）较好地解决了模糊线性回归模型对界外值敏感的问题 相似文献
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管理层薪酬激励的非参数分析 总被引：1，自引：0，他引：1
利用非参数计量经济学模型对我国上市公司2003年和2004年高管人员的薪酬和公司业绩之间的关系进行了分析,发现非参数模型的拟合程度要好于传统的线性回归模型,非参数模型的均方误差也明显小于线性回归模型,而且非参数模型的近期预测效果也好于线性回归模型。 相似文献
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RBF神经网络是一种模仿动物神经网络行为特征,进行分布式并行信息处理的算法数学模型,它可以把线性不可分问题转化为线性可分问题,具有逼近任意复杂非线性映射的功能。本文利用RBF神经网络工具箱解决分类方面的问题。 相似文献
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本论文利用统计中的线性回归模型的方法建立了评价模型。然后,开展调查、获取数据,利用所得的数据估计模型中的参数,求出关于综合指标的线性评价模型。最后,利用所建立的模型对具体的网站进行评价,以检验模型的合理性。 相似文献
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《中国专利与商标》2006,(4)
It is a relatively common phenomenon to limit technical features with parameter range in patent claims. It is argued in this article that the parameter range should be distinctly divided into single parameter range and whole parameter range depending on the different mode and function of limitation. Each and every parameter in a single parameter range may independently achieve a technical effect, and limit one embodiment alone; while a single parameter in the whole parameter range cannot independently ac... 相似文献
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In nonlinear parameter estimation local sensitivity assessment; conventionally measured by the first-order derivative of the predicted response with respect to a parameter of interest fails to provide a representative picture of the prediction sensitivity in the presence of significant parameter co-dependencies and/or nonlinearities. In this article we derive the profile-based sensitivity measure developed by Sulieman et al. (2001, 2004) [1] and [2] in the context of model re-parameterization. In particular, the so-called predicted response re-parameterization is shown to ultimately lead to the profile-based sensitivity coefficient defined by the total derivative of the model predicted response with respect to a parameter. Although inherently local, the profile-based measure is shown to handle simultaneous perturbations in parameter values while accounting for their co-dependencies. Thus the proposed measure possesses a central property of a global sensitivity measure and so it is considered hybrid local-global measure.The global Fourier amplitude sensitivity test (FAST) is added to the analysis and compared with both marginal and profile-based sensitivity methods. The Fourier sine amplitude is utilized here as a first-order sensitivity measure and shown to be directly linked to the local sensitivity coefficient averaged over all ranges of parameter uncertainties and so it is also considered hybrid local-global measure. The comparisons are explained by three compelling model cases with different degrees of parameter co-dependencies and nonlinearities. 相似文献
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对机载单天线SAR/GMTI模式下动目标参数估计精度较低的问题进行研究.首先,用多普勒频移量和距离走动量来估计动目标径向速度,根据估计的结果校正距离走动.然后,用改进的反射特性位移法来估计动目标的多普勒调频率,在不存在加速度时估计出动目标方位向速度.这样就可以在进行动目标参数估计的同时实现聚焦成像.仿真结果验证了该方法的有效性. 相似文献
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巴斯卡分布NB(r,p)中参数r的估计 总被引：1，自引：0，他引：1
巴斯卡分布NB（r,p)是重要的离散型分布之一，当r已知时，巴斯卡分布NB（r,p)中未知参数p(p∈（0，1））的估计是众所周知的。本文分别在p已知和未知的条件下，研究了参数r(r∈（1，2，…}）的估计，并得到了它们的渐近性质。 相似文献
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扑克检测是一项基本的统计检测,用来判断一个二进制序列是否随机。除了二进制序列外,该检测需要额外的参数输入。研究了如何确定扑克检测参数值的合法范围,并通过大量实验,发现存在某些代表性参数值可以代替所有参数进行扑克检测。 相似文献
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The Hammerstein–Wiener model is a nonlinear system with three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. For parameter learning of the Hammerstein–Wiener model, the synchronous parameter learning methods are proposed to learn the model parameters by constructing hybrid model of the three series block, such as over parameterization method, subspace method and maximum likelihood method. It should be pointed out that the aforementioned methods appeared the product term of model parameters in the process of parameter learning, and parameter separation method is further adopted to separate hybrid parameters, which increases the complexity of parameter learning. To address this issue, a novel three-stage parameter learning method of the neuro-fuzzy based Hammerstein–Wiener model corrupted by process noise using combined signals is developed in this paper. The combined signals are designed to completely separate the parameter learning issues of the static input nonlinear block, the linear dynamic block and the static output nonlinear block, which effectively simplifies the process of parameter learning of the Hammerstein–Wiener model. Parameter learning of the Hammerstein–Wiener model are summarized into the following three aspects: The first one is to learn the output static nonlinear block parameters using two sets of separable signals with different sizes. The second one is to estimate the linear dynamic block parameters by means of the correlation analysis method, the unmeasurable intermediate variable information problem is effectively handled. The final one is to determine the parameters of the static input nonlinear block and the moving average noise model using recursive extended least square scheme. The simulation results are presented to illustrate that the proposed learning approach yields high learning accuracy and good robustness for the Hammerstein–Wiener model corrupted by process noise. 相似文献
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Quantitative feedback theory (QFT) is a powerful design technique for robust feedback control systems with plant uncertainties. In applying QFT to design robust feedback control systems, the generation of plant templates is an essential step. For a system with affinely dependent parameters and the parameter domain is a box, it is well known that the boundary of a plant template is included in the image of the set of edges of the parameter domain box. One can obtain the plant template from the image of the set of edges. However, this approach to the generation of the plant template leads to heavy computational burden since it wastes much computational effort computing the images of points on edges which lie in the interior of the plant template. In this paper, an efficient algorithm is proposed to identify, from an edge of the parameter domain box, the set of parameter points whose image lies in the interior of the plant template. The computational burden for generating the plant template thus can be obviously reduced by eliminating the identified sets of parameter points in the plant template generation procedure. Numerical examples are included to illustrate the efficiency of the algorithm. 相似文献
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《Journal of The Franklin Institute》2014,351(12):5565-5581
This paper is concerned with the identification problem of linear parameter varying (LPV) time-delay systems. Due to inherent nonlinearity, the industrial processes are often approximately described by an LPV model constructed by synthesizing multiple local models. Time-delay is commonly experienced in industrial processes and it can be parameter varying or constant in the process model. The multiple model identification of LPV systems with parameter varying or constant time-delay is formulated in the scheme of the expectation-maximization (EM) algorithm and the parameter varying property and the time-delay property of the process are handled simultaneously. The irrigation channel example and high purity distillation column example are used to present the effectiveness of the proposed method. 相似文献