*--- --- --- --声明--- --- --- -----*/ /* VC6.0下运行通过 此程序为本人苦心所做，请您在阅读的时候，尊重本人的 劳动。可以修改，但当做的每一处矫正或改进时，请将改进 方案，及修改部分发给本人 (修改部分请注名明：修改字样) Email: jink2005@sina.com QQ: 272576320 ——初稿完成：06-5-27 jink2005 补充： 程序存在问题： (1) follow集不能处理：U->xVyVz的情况 (2) 因本人偷懒，本程序为加入文法判断，故 输入的文法必须为LL(1)文法 (3) 您可以帮忙扩充：消除左递归，提取公因子等函数 (4) …… */ /*-----------------------------------------------*/ /*参考书《计算机编译原理——编译程序构造实践》 LL(1)语法分析,例1: ERTWF# +*()i# 文法G[E]:(按此格式输入) 1 E -> TR 2 R -> +TR 3 R -> 4 T -> FW 5 W -> * FW 6 W -> 7 F -> (E) 8 F -> i 分析例句：i*(i)# , i+i# 例2： 编译书5.6例题1 SHMA# adbe# S->aH H->aMd H->d M->Ab M-> A->aM A->e 分析例句：aaabd# */
上传时间： 2016-02-08
上传用户：ayfeixiao
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：lindor
异步电机矢量控制程序，包括坐标变换，电流采样，pi调节等功能，用的是汇编语言
上传时间： 2014-06-05
上传用户：koulian
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
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.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
标签： algorithms problems Several trivial
上传时间： 2014-01-20
上传用户：royzhangsz
matlab遗传算法工具箱函数及实例讲解2 【问题】在－5<=Xi<=5,i=1,2区间内，求解 f(x1,x2)=-20*exp(-0.2*sqrt(0.5*(x1.^2+x2.^2)))-exp(0.5*(cos(2*pi*x1)+cos(2*pi*x2)))+22.71282的最小值。
上传时间： 2013-12-31
上传用户：gtzj
在过程控制中，按偏差的比例（P）、积分（I）和微分（D）进行控制的PID控制器（亦称PID调节器）是应用最为广泛的一种自动控制器。它具有原理简单，易于实现，适用面广，控制参数相互独立，参数的选定比较简单等优点；而且在理论上可以证明，对于过程控制的典型对象──“一阶滞后＋纯滞后”与“二阶滞后＋纯滞后”的控制对象，PID控制器是一种最优控制。PID调节规律是连续系统动态品质校正的一种有效方法，它的参数整定方式简便，结构改变灵活（PI、PD、…）。
标签： 过程控制
上传时间： 2014-01-18
上传用户：561596
最新版本1.7.老外编的,编辑梯子逻辑到PIC16或者AVR 代码。 如下内容 处理器被支持： *PIC16F877 *PIC16F628 *PIC16F876(未受测试) *PIC16F88(未受测试) *PIC16F819(未受测试) *ATmega128 *ATmega64 *ATmega162(未受测试) *ATmega32(未受测试) *ATmega16(未受测试) *ATmega8(未受测试)
上传时间： 2013-11-30
上传用户：hakim
matlab实现的多个基础程序和报告并有流程图（1） 绘出正弦信号波形及频谱。 （2） 单极性归零（RZ）波形及其功率谱，占空比为50％。 （3） 升余弦滚降波形的眼图及其功率谱。滚降系数为0.5。发送码元取值为0、2。 （4） 最佳基带系统的Pe~Eb\No曲线，升余弦滚降系数a=0.5，取样值的偏差是Ts/4。 （5） Pe~Eb\No，升余弦滚降系数a=0.5，取样时间无偏差，但信道是多径信道，C(f)=abs(1-0.5*exp(-j*2*pi*f*dt))，dt=Ts/2。 （6） 仿真数字基带传输系统，包括输入、输出信号波形及其功率谱，眼图（升余弦滚降系数a=0.5），Pe~Eb\No曲线，取样时间无偏差。
上传时间： 2014-01-22
上传用户：aix008
