whole_system_new_source

说明：  Includes source codes to find DFT of a sequence, to find odd and even components of a signal, to perform PSK, FSK, QPSK.

对NYSE股票日收盘价格，运用GARCH或者ARCH模型进行分析，并预测2012年5月1日到2012年5月9日的价格。将预测值与真实值对照，比较误差。(Closing stock price on the NYSE, using GARCH or ARCH models to analyze and predict the price May 1, 2012 to May 9, 2012 in. The predictive value and the true value control, more errors.)

It is a 4-bit adpcm. This is a simple illustration of how compression can be achieved more than pcm.

MLP neural network with matlab

风机控制系统-springer 出版社-增益调度控制 (Wind Turbine Control Systems- Principles,Modelling and Gain Scheduling Design)

压缩文件中给出了已知系统传递函数及输入信号的情况下，求解系统输出响应的四种方法，即差分方程法，卷积法，DFT法以及滤波器设计方法；此外还包含创建窗及其频率响应的matlab设计。(Known compressed file system transfer function is given and the case of the input signal, the output response of the system to solve the four methods, namely the differential equation method, convolution, DFT method and the filter design methods Also included creating windows and its frequency response matlab design.)

to detect the energy in cognitive radio

信号处理中的旋转不变子空间法，一个很有用的程序，Matlab实现界面友好，实现串口的数据采集，相关分析过程的matlab方法，数据模型归一化，模态振动，自己编的5种调制信号，正确率可以达到98％。 ( Signal Processing ESPRIT method, A very useful program, Matlab to achieve user-friendly, Achieve serial data acquisition, Correlation analysis process matlab method, Normalized data model, modal vibration, Own five modulation signal, Accuracy can reach 98 . )

说明：  控制进水阀S1和出水阀S2，使水箱水位保持在目标水位O处。使用模糊控制控制水箱液位。(Tank water level Fuzzy RBF control)

稳态kalman滤波算法仿真通式 本程序考虑线性离散时不变随机系统。系统模型为x(t+1)=fai*x(t)+gama*w(t) y(t)=H(t)*x(t)+v(t)。有6个参数：状态转移阵fai，输入噪声系数gama，观测阵H，输入 噪声方差Q，观测噪声方差R，观测y( Steady-state kalman filtering algorithm simulation program to consider the general form linear discrete time-invariant stochastic system. System model x (t+1) = fai* x (t)+ gama* w (t) y (t) = H (t)* x (t)+ v (t). There are six parameters: state transition matrix fai, input noise figure gama, observation matrix H, enter noise variance Q, observation noise variance R, observation y)