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Description: M=8PSK通信系统的MonteCarlo仿真，检测器的检测算法按照最接近接收信号相位的方法选为信号点。-M = 8PSK communication system Monte Carlo simulation, detector detection algorithm according to the method closest to the received signal phase is selected to signal point.
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Size: 3072 |
Author: maomaoyu |
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Description: 本设计主要对4PSK调制方式的信号，利用MATLAB的m文件进行最佳接收机的设计与仿真。对输入的叠加噪声的4PSK调制信号进行接收，利用相关解调器来实现信号解调，及最大似然准则来实现检测器。在相关解调器中，接收信号分别与基函数 和 相乘再积分。在检测器中，利用相位来判断输出，从而最终得到接收的数据。采用随机二进制数通过4PSK调制后叠加高斯白噪声再对设计的接收机进行测试，从测试的结果可看出，在信噪比大于-8dB时，误码率为0，说明该接收机较好的实现了抗噪声性能。-The design of the main 4PSK modulation mode of the signal, the use of MATLAB m file for the best receiver design and simulation. The 4PSK modulated signal of the input superimposed noise is received, and the demodulator is realized by the relevant demodulator, and the maximum likelihood criterion is used to realize the detector. In the associated demodulator, the received signal is multiplied by the basis function and multiplied by the basis function. In the detector, the output is judged by the phase, and finally the received data is obtained. Using the random binary number through 4PSK modulation after the superposition of white noise and then the design of the receiver to test the test results can be seen in the signal to noise ratio greater than-8dB, the bit error rate is 0, indicating that the receiver is better The realization of the anti-noise performance.
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Size: 21504 |
Author: kangyuxiang |
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Description: 深度神经网络在测试时面对如此大的网络是很难克服过拟合问题的。
Dropout能够很好地解决这个问题。通过阻止特征检测器的共同作用来提高神经网络的性能。这种方法的关键步骤在于训练时随机丢失网络的节点单元包括与之连接的网络权值。在训练的时候，Dropout方法可以使得网络变得更为简单紧凑。在测试阶段，通过Dropout训练得到的网络能够更准确地预测网络的输出。这种方式有效的减少了网络的过拟合问题，并且比其他正则化的方法有了更明显的提升。
本文通过一个简单的实验来比较使用Dropout方法前后网络的性能优劣情况。(It is difficult to overcome the problem of fitting a deep neural network in the face of such a large network in testing.
Dropout can solve this problem well. The performance of the neural network is improved by preventing the common action of the feature detector. The key step of this method is that the node unit of the random loss network consists of the network weights connected to it during the training. In training, the Dropout method can make the network more compact. In the test phase, the network trained by Dropout can predict the output of the network more accurately. This method effectively reduces the over fitting problem of the network, and has a more obvious improvement than the other regularization methods.
In this paper, a simple experiment is used to compare the performance and performance of the Dropout method.)
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Size: 311296 |
Author: 转角的狐狸 |
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Description: 讲述并网逆变器中全软件锁相环的设计与实现，，即检测基波正序分量的电网电压不平衡和扭曲的条件下。明确地,提出了一种积极的基于一种新的序列检测器双同步坐标系的解耦锁相环（双dq–PLL），完全消除了检测误差传统的同步参考框架（SRF–锁相环PLL）(and implementation of all software phase-locked loop in grid connected inverter is described, that is, detecting the positive and negative component of the fundamental wave under unbalanced grid voltage conditions. Explicitly, a positive decoupling phase locked loop (double DQ - PLL) based on a new sequence detector dual synchronous coordinate system (double DQ - PLL) is proposed, which completely eliminates the traditional synchronous reference frame (SRF - PLL PLL) for detection error.)
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Size: 4512768 |
Author: 醚 |
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Description: 针对于微弱信号检测使用相敏检波器将微弱信号放大并重现，代码简单，适合初学者(For weak signal detection, phase sensitive detector is used to amplify and reproduce the weak signal. The code is simple and suitable for beginners)
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Size: 1024 |
Author: FollowTheMap |
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