complex-value造句

• A method for ica for complex - valued sources
  一种针对复值信号的独立分量分析方法
• A novel algorithm is proposed for training complex - valued neural networks
  摘要提出了一种新型复数前馈神经网络的学习算法。
• Based on the second order statistics , an algorithm is proposed to separate mixed complex - value signals online
  摘要基于二阶统计量，对在线分离复值混合信号法进行了研究。
• The full - rank matrix is employed to find the complex - valued weights between hidden and output layers by the least mean square algorithm
  利用这个满秩矩阵，通过最小平方算法就可以求得隐层和输出层之间的复数权值。
• To improve learning speed , a novel method for properly initializing the parameters ( weights ) of training complex - valued neural networks is proposed
  摘要为了改善学习速率，提出了一种确定复数神经网络初始权值的新颖方法。
• Because the initialized weights are optimized , the training accuracy and the learning speed are improved a lot for training complex - valued neural networks
  初始权值的优化，使得该算法可以有效地提高复数神经网络的训练速度和计算精度。
• An online algorithm for complex independent component analysis was proposed based on the complex nonlinear functions and the decorrelation of two complex - valued vectors
  摘要基于复向量不相关特性和复值非线性函数，提出一种在线复值独立分量分析算法。
• The complex - valued weights between hidden and output layer are updated by solving linear system based on finding the complex - valued weights between input and hidden layer
  当输入层和隐层之间的权值计算出来后，就可以通过求解线性方程组得到隐层和输出层之间的权值。
• The main idea is to make full use of the decorrelation of two complex - valued vectors in generating independent components by non linear decorrelation
  结合非正则复向量的协方差矩阵和伪协方差矩阵构造出了新的代价函数，进而提出新算法，通过复非线性不相关，从混合信号中提取出复值独立分量。
• The gabor transform is one of the most important schemes for time - frequency analysis . since the traditional gabor transform is complex - valued , it ' s real - time applications were limited due to the high complexity involved in the computation of the complex - valued transform
  Gabor变换是重要的时频分析方法之一，由于传统gabor变换为复值变换，计算复杂度高、计算量大，限制了gabor变换的实时应用。
• Since a pseudo - covariance matrix of the observed signals supply additional constraints if the sources are assumed to be independent and improper , it can be proved that the second order statistics is sufficient to separate the mixed complex - value signals for both stationary and non - stationary sources
  假设源信号是独立的且非常态，信号的伪协方差矩阵能增加约束条件，从而可证明二阶统计量能够完全分离复值混合信号，而且对信号是否平稳不作要求。
• Considering the complex - value signals processing or parameter estimation , the dissertation study the complex - value least square method . the paper put forward complex recursive least square method either weighted data or using forgetting factor to avoid data saturation and increase the observed data reliability
  考虑到信号处理中大量存在的复数域参数估计问题，研究了复数域最小二乘参数估计方法，提出了既加权又使用遗忘因子的复最小二乘估计递推算法。
• Because the difference of real - valued and complex - valued system , the different neuromime is adopt in different system . in addition , this paper design two kinds of transmission function to different input signal . the new algorithm make up the flaw such as small application domain and difficult chose of parameter
  文中针对实数和复数系统的差异提出了两种不同的网络神经元结构，并且根据传输信号的差异设计了两种传输函数，弥补了原来算法应用范围小、参数不易选取的缺陷。
• In chapter 4 , we discussed the method which involves segmenting a complex - valued sar image into patches , focusing each patch separately in two autofocus ways , pga and shear averaging , and measuring the sharpness increase in the focused patches . then judge if there ' re moving targets through the sharpness increase
  第四章先详细阐述了一种动目标检测方法，这种方法先将一幅复值sar图像分成很多小块，然后对每个小块分别运用相位梯度自聚焦或剪切平均自聚焦算法进行单独的聚焦处理，测出每一小块处理前后图像强度的变化，再根据一定标准判断是否存在动目标。
• In chapter 3 a complex - valued neural network method for adaptive complex communication channel equalization is applied . after comparing the equalization result of gmsk and 4 - qam signal , it shows that the method is effective when gmsk signals pass the complex communication channel and such structure can save the hardware source
  第三章进一步引申第二章的内容，采用了一种复神经网络的自适应均衡的方法，通过比较gmsk和4 - qam信号的均衡结果，表明这种方法对gmsk信号通过复信道均衡的有效性，并且所采用的结构可以节省硬件资源。