PID1

Windowed mutual information

Smith Nguyen Studio. Smith Nguyen eBooks. Smith Nguyen Studio. Smith Nguyen eBooks. Smith Nguyen Studio. Smith Nguyen eBooks.

基于Matlab的移动通信中多径衰落信道的仿真基于多径传输和多普勒频移的 瑞利（Rayleigh）信道的仿真 主要考虑不同条件下的仿真(Matlab-based mobile communications in the multipath fading channel simulation based multi-path transmission and Doppler frequency shift of the Rayleigh (Rayleigh) channel simulation the main consideration of the simulation under different conditions)

matalab2010经典超强教程，很好的说明了matalab的用法(Matalab2010 classic super tutorials, very good that the matalab usage)

ERA模态参数提取方法，根据特征参数提取(MOdal ERA method)

用Matlab实现重叠保留法计算循环卷积的程序实例，课堂上完成的，希望有用(Using Matlab to achieve overlapping reservation method to calculate cyclic convolution procedure example, the completion of the classroom, and I hope useful)

实验内容 信号与系统试验报告 自己的作业 1.设有两个信号： 要求x(t)和h(t)采样形成离散序列， 参数选择如下： 采样率Δ=4ms， 频率f=30Hz， a=2f2ln(M), M=2.5 在MATLAB下实现连续信号离散化， 并绘制出离散形式的信号波形。 (Experimental signal and system test the contents of the report of one)

在matlab6.5下完成的直流电机闭环调速(Matlab6.5 completed in under the closed-loop DC motor speed control)

有记忆预失真处理程序，可以实现对PA转换下饱和失真的处理(Predistortion with memory processes can be achieved under the PA saturation distortion conversion process)

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)