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Home > chinese-english > "输入矩阵" in English

English translation for "输入矩阵"

input matrix

Related Translations:
访问矩阵:  access matrix
原矩阵:  original matrix
连接矩阵:  adjacency matrixco ectivity matrixconnected matrixconnection matrixconnectivity matrix
透射矩阵:  transmission matrix
概率矩阵:  probability matrix
基矩阵:  basic matrixbasis matrix
正定矩阵:  positive definite matrixpositively definite matrix
方差矩阵:  covariance matrixdispersion matrixvariance matrix
矩阵行列式:  matrix determinant
置换矩阵:  permutation matrix
Example Sentences:
1.For a large class of input matrix pairs , the algorithm improves the known exponents
对一大类的输入矩阵对,该算法降低了已知的矩阵乘法指数。
2.Moreover , we compare the performances between these two estimations for a linear model with random input
更进一步,我们研究了推广线性模型(即输入矩阵为随机矩阵)下这两种估计的性能差异。
3.First , it is assumed that the polytope exists in the system matrix , input matrix and measure matrix of the stale space model
首先,假定多胞型不确定参数存在于状态空间模型的系统矩阵、输入矩阵和量测矩阵中。
4.( 1 ) equation is deduced when input is complete correlation , frequency response function has not solution ( order of numerator of polynomial need not be defined ) . we can directly use superposition principle of linear system to obtain response without solving frequency response function
( 1 )当输入部分相关时,输入矩阵为非对角矩阵,其频响函数不能直接求得,本文推导了解决此类问题的公式。
5.The norm - bounded parametric uncertainties entered into system matrices , input matrices and interconnections in all of the t - s fuzzy subsystems . dpdc controllers are designed for the uncertain interconnected fuzzy large - scale systems and lmi - based decentralized robust stabilizable conditions for uncertain interconnected fuzzy large - scale systems are developed
所考虑的参数不确定性以范数有界的形式出现在各个子模糊系统的系统矩阵、输入矩阵及输出矩阵中,本论文讨论了该类不确定性关联大系统dpdc控制器的设计问题。
6.For a general linear model ( input matrix is deterministic ) , under a certain conditions on variance matrix invertibility , the two estimates can be identical provided that they have the same priori information on the parameter under estimation . even if the above information is unknown only for the optimally weighted ls estimate , the sufficient condition and necessary condition , under which the two estimates are identical , is derived . more significantly , we know how to design input of the linear system to make the performance of the optimally weighted ls estimation identical to that of the linear minimum variance estimation in case of being lack of prior information
在一般线性模型(即输入矩阵为确定性)下,当两种估计都利用有关被估参数的先验信息时,二者在方差阵可逆的一定条件下可达到一致;当最优加权最小二乘估计不利用此先验信息时,存在二者一致的充分条件和必要条件,进而找到一种设计输入矩阵的方法,使得在先验信息缺乏的条件下,仍可利用最优加权最小二乘估计达到与线性最小方差估计一样优越的估计性能。
7.Within the framework of sparse bayesian learning , the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels , and the improved locality preserving projection ( llp ) is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time
在稀疏贝叶斯学习的框架下,该算法首先以多核形式自适应结合全局核函数和局部核函数扩展相关向量机,然后应用改进的保局投影来约简多核输入矩阵的列维数以减少训练时间。
8.This method can guarantee the solution matrix of sylvester equation to be inverse and the sum of the input gain norm and the observer gain norm is the minimum . for the linear systems with unknown parameters , we identify the parameters using hopfield network , then design the observers using the identified parameters , the exponential convergence of adaptive observer is also proved . for the linear time - varying systems , a new network to solve the time - varying sylvester equation is proposed , we analysis it ' s convergence and robustness , then , deign the linear time - varying observer using this network model , and we discuss the convergence of the observer and ruboustness to unknown match parameters
同时保证了sylvester方程的解矩阵的可逆性和观测器的增益矩阵与输入矩阵范数的和最小;在设计线性时不变自适应观测器时,首先利用系统的输入、输出数据设计一个hopfield网络参数估计器,进一步设计状态观测器,证明了参数估计器和状态观测器的指数收敛性;为了仍然从神经优化计算的角度设计线性时变系统的状态观测器,最后介绍了一种求解时变sylvester矩阵方程的神经网络模型,分析了它的收敛性和鲁棒性,然后利用该网络设计时变状态观测器,进一步讨论该观测器的在系统存在未建模不确定和外部噪声时的鲁棒性;最后给出了一种基于分离性原理和hopfield网络观测器的状态反馈闭环系统的结构,分析了该闭环系统的特点;对于每一种设计方法都给出了相应的数值仿真例子来进一步表明所提方法的可行性和有效性。
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