免疫算法

该文档详细讲述了关于matlab在通信仿真中的具体应用，包括模拟调制中的信号处理以及数字信号的抽样量化过程的matlab实例(This document describes in detail the communication matlab simulation on the specific applications, including analog modulation of the signal processing and digital signal sample quantization process of matlab examples)

说明：  关于MATLAB能量检测频谱感知算法的研究代码(MATLAB energy detection on spectrum sensing algorithm code)

数字信号处理 各种滤波器的设计，中南大学电子信息工程 大三的课程设计全部源代码和实验报告，欢迎下载！(various digital signal processing filter design, Zhongnan University of Electronics and Information Engineering, the design of the church all of the source code and laboratory reports, download welcome!)

非参数的功率谱估计包括四个，周期图法，Blackman-Turkey,Welch-Bartell,改进的周期图法，输入信号为白噪声通过切比雪夫滤波后的信号(Non-parametric estimation of the power spectrum consists of four, periodogram, Blackman-Turkey, Welch-Bartell, improved cycle diagram method, the input signal is white noise ratio of the signal by cutting Schiff filtered)

数学建模：下料问题数学建模：下料问题数学建模：下料问题数学建模：下料问题数学建模：下料问题(Mathematical modeling: automatic lathes)

CPM调制的线性均衡器，使用维特比判决器进行判决。(CPM modulation linear equalizer, the use of Viterbi browser sentencing verdict.)

高阶统计量的基础学习资料，包括高阶统计量在信号处理方面的一些应用(High order statistics based learning materials, including high order statistics in signal processing some of the application)

串联模糊PID，搭建模糊PID框架，并与普通PID对比，演示模糊控制特点(Series fuzzy PID, fuzzy PID frame structures and contrast with the ordinary PID, fuzzy control features demo)

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

over shoot rise time in matlab