lms

matlab的调制解调， matlab的调制解调(matlab modem, matlab modem)

this is simple ann code

这是QAM信号和PSK信号的解调程序，其中包括16-QAM,32QAM,64-QAM和M进制PSK信号。(This is PSK and QAM signal demodulation process, including 16-QAM, 32QAM. 64-QAM and M-ary PSK signals.)

合成孔径雷达的成像仿真程序，效果比较好，算法优化较好。(Synthetic aperture radar imaging simulation program, better, better optimization algorithms.)

进退法 罚函数法 0.618方法程序过程 分别用matlab以及c两种语言(0.618 retreat France penalty function methods and procedures to process)

将PID 控制与模糊控制的简便性、灵活性以及鲁棒性融为一体,构造了一个参数自整定模糊PID 控制器. 通过模糊控制规则在线调整PID 控制器的参数,并利用MATLAB 语言结合具体实例方便而快速地实现了该控制器的计算机仿真. 仿真结果表明:该控制方法提高了控制系统的动、静态特性,使系统获得了较好的性能.(The PID control and fuzzy control of the simplicity, flexibility and robustness of the integration, construction of a parameter self-tuning fuzzy PID controller. Through the fuzzy control rules online adjust the parameters of PID controller, and use specific examples of MATLAB language and quickly realize the convenience of the controller)

这是关于直接序列扩频通信系统的DS-CDMA方式的仿真设计，系统主要分为发送机和接收机两部分，包括17个可用于SIMULINK仿真的S-FUNCTION代码M文件，和一个MATLAB源程序的DOC文件，程序都有详细的注释。(This is a direct sequence spread spectrum communication system on the DS-CDMA mode simulation design, the system can be divided into two parts, transmitter and receiver, including 17 for the S-FUNCTION SIMULINK simulation of M code files, and a MATLAB source code The DOC file, the program has detailed notes. 朗读 显示对应的拉丁字符的拼音 字典- 查看字典详细内容名词0.directness0.immediacy形容词0.direct0.immediate0.first-hand0.plump副词0.straight0.right0.slap )

SISO控制系统性能评价harris指标m文件+mdl文件，其中运用遗忘因子算法对线性回归算法（LR）进行改进(ILR)。希望对大家有所帮助(SISO control system performance evaluation index m harris file+ mdl files, which use forgetting factor algorithm for the linear regression algorithm (LR) to improve (ILR). We want to help)

EMD分解及HHT分解，主要功能是能把混合信号分解成其所含的各频率成分(HHT EMD decomposition and decomposition, the main function is to be able to decompose mixed signal into its frequency components contained in the)

该文件是SVM神经网络的源程序，可以用于对数据进行分类预测(The file is SVM neural network source, can be used for data classification forecast)