adaptive-scaling

1、本程序用于仿真里德-穆勒（Reed-Muller）码在AWGN信道中的性能，调制采用bpsk 2、理论请参见《差错控制编码》（第二版），Shu Lin（美）编，晏坚等（译）一书P70-75.且仿真结果与书中图4-2完全一致 3、BPSK_AWGN_RM_Code.m为主程序，点击运行即可 4、The_creation_of_RM_code.m和RM_Decode.m分别为RM码的编、译码程序，而Majority_logic_decision.m用于译码中的大数判决，combine_dunction.m用于计算组合数。(1, the procedure for the simulation Reed- Muller (Reed-Muller) codes in AWGN channel performance, modulation using bpsk 2, theory, see " Error Control Coding" (second edition), Shu Lin (America) series , Yan Jian, etc. (translated) book P70-75., and the simulation results consistent with the book Figure 4-2 3, BPSK_AWGN_RM_Code.m-based program, click Run to 4, The_creation_of_RM_code.m and RM_Decode.m were RM compiled code decoding process, and in large numbers for decoding Majority_logic_decision.m judgment, combine_dunction.m used to calculate the number of combinations.)

小脑模型是一种表达复杂非线性函数的表格查询自适应神经网络，此列例举了用于非线性函数逼近(CMAC is a complex nonlinear function of the expression of the form query adaptive neural network, this column cites for nonlinear function approximation)

直流双闭环调速系统，是用神经网络作为控制器的(DC double-loop speed control systems, neural networks are used as a controller)

本程序为EEMD分解程序，用于信号的EEMD分解(This procedure for the EEMD decomposition process, a signal for EEMD decomposition)

计算电力系统随机生产模拟的简单程序，包括常规卷积法、半不变量法和等效电量函数法(Simple procedure for calculation of power system probabilistic production simulation)

短时傅里叶变换的例子，matlab语言,简洁实用(shot time Fourier transform using matlab)

对机载SAR系统点目标进行模拟仿真，利用RD算法分别对零斜视角、小斜视角、大斜视角进行成像。(This experiment is for point target simulation of SAR system, and we use RD algorithm to achieve point target imaging, respectively, zero oblique angle, a small oblique angle,and a large squint angle imaging.)

相依光栅谱型的matlab模拟。有具体的指导文件，详细的编写过程。(Dependent grating spectral type matlab simulation. There are specific guidance documents, detailed preparation process.)

qpsk调制解调 源文件，在现在的毕业设计和课程设计中都有要求的(modem qpsk source file, in the current school design and curriculum design have requested)

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)