coefficient vector meaning in Chinese
系数向量
Examples
- In our proposed method , both the objective function and the perfect reconstruction condition are expressed as a quadratic function of the prototype filter coefficient vector
该方法中，目标函数和完全重构条件均被表示成为原型滤波器系数矢量的二次函数形式。 - By using the lagrange multiplier approach , the design procedure is formulated as solving the linear equation iteratively to obtain the desirable prototype filter coefficient vector
使用拉格朗日乘数方法，算法通过迭代求解线性方程来获得期望的原型滤波器系数矢量。 - We raised a new model that we disassemble the character into several parts , which could be recognized by computer topologically based on the high - frequency wavelet coefficients vector , disregarding the traditional extraction method that used the statistical or structural feature based on the individual pixel in the 2 - dim plane of character . moreover , the concept of multi - dim cognizing feature model was put forward by encoding the character , according to its " location and run - length information . the information confusion and redundancy could be largely eliminated , as leaded to the improving of the preciseness when recognizing the character
克服以往结构、统计方法在字符特征提取中无法剔除噪声、偏移等冗余信息的不足，以认知的新思路分析图像，给出基于小波子图的笔划定义，给出一种注重反映字符部分最为重要的笔划的类型、数量、游程、位置特征，改进了基于字符二维图像的统计与结构特征提取方法因变形，畸变造成信息混淆和冗余；给出了提取多属性字符认知特征的方法和识别机制，实验表明，该方法能有效的识别字符； 3 - The inner product of the mapping value of the original data in feature space is replaced by a kernel function , and the weights of each neuron can be initialized and updated by initializing and updating the combinatorial coefficient vector of each weight in the algorithm of ksom , so some intuitive and simple iteration formulas are obtained
该算法以核函数代替原始数据在特徵空间中映射值的内积，并且神经元权值向量的初始化和更新都可由其组合系数向量表示，从而获得了直观而简单的迭代公式。 - Chapter four introduces the basic theories of continue hidden markov models ( chmm ) . the new method of faults diagnosis based mixture density chmms directly by the vibration ar coefficients vectors of rotating machine is proposed , and then the dynamic patterns presented in run - up process of rotor machine are successfully recognized . at last compares the two faults diagnosis methods of dhmm and chmm , and points out the advantages and disadvantages of the two methods
第四章：在连续隐markov模型( chmm )的基本理论基础上，提出了直接利用振动信号ar系数特征矢量序列建立混合密度chmm的故障诊断新方法，并对转子升速过程的振动模式进行了成功的识别；对dhmm和chmm故障诊断方法进行了对比分析，指出dhmm方法具有算法稳定、计算速度快、识别精度高等特点，对于chmm方法只要通过合理选择特征参数也能得到高的识别精度。