fpdSNR1

Dragon Forth (DF) is a powerful tool for creating applications for Palm OS of any complexity. The name Forth means fully compatibility with ANS 94 standard.

关于bpsk调制和解调，进行模拟调制，自行编写绝对正确(About bpsk modulation and demodulation, to simulate modulation, self-preparation is absolutely right)

说明：  最基础的灰色预测的matlab源程序，可以直接运行(Grey prediction program by matlab)

说明：  一个模糊神经网络的参数训练程序，希望对大家有所帮助(The parameters of a fuzzy neural network training procedures, we want to help)

Parzen窗法，由样本求概密估计，例为一维正态分布和双峰均匀分布，内附随机数生成的原理说明。(Parzen window method, by the sample density estimate for estimated Example for one-dimensional normal distribution and bimodal uniform distribution, random number generator containing a description of the principle.)

大地电磁一维正演程序 有限差分的程序，希望能对初学者有用(Magnetotelluric one-dimensional forward program of finite difference, the hope can be useful for beginners)

simulation of the effect of a filter on a signal, showing amplitude and phase spectra, in three cases, varying its bandwidth

MACHINE SYNCHRONE A AIMANT PERMANANT ALIMENTE CONNECTEE A UNE EOLIENNE

数字滤波器的各种结构的C程序实现，包含格型滤波器。本文没有讨论复杂的数学方法及公式。相信对正在为滤波器结构实现烦恼的人有所帮助。(Various structures of digital filter C program, including lattice filter. This article does not discuss the complex mathematical methods and formulas. I believe the troubles are achieving for the filter structure of human help.)

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates 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.)