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EMdemo
- n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic
rjMCMCsa
- 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
nefcon
- 模糊神经网络采用matlab编程 o install NEFCON follow these steps: 1. Unpack the tar file NEFCON.TAR into your MATLAB working directory: tar xf NEFCON.TAR 2. Start MATLAB 3. Change to the installation directory. 4. Change to the NEFCON directory. 5. Start the STA
antnet-1.1.rar
- Programming language: Developed in Omnet++. Comment: The model implements the AntNet routing algorithm proposed in: G. Di Caro and M. Dorigo. AntNet: Distributed Stigmergetic Control for Communications Networks. Journal of Artificial Intelligence Re
gradient_demo.m.tar
- 梯度下降算法实现程序，梯度下降算法实现程序-Gradient descent algorithm procedure
xy2libsvm.m.tar
- 将二维数据转换成libsvm格式的程序，matlab格式文件，可作函数调用。-Will be two-dimensional data into libsvm format procedures, matlab format and can be used for function calls.
fisher.m.tar
- 模式识别经典算法：fisher判别分析的二次优化形式实现。-Classic pattern recognition algorithms: fisher discriminant analysis achieved in the form of quadratic optimization.
script1.m.tar
- My first code in matlab for networks topology
MOS_BPNN.m.tar
- 是BP神经网络的matlab实现版，有助于理解神经网络的算法过程-Is BP neural network matlab realize edition, helps to understand the process of neural network algorithm
huidu_li.m.tar
- 此代码为本人在做灰色关联度分析时所用的matlab代码，所用实验数据也在，可以直接运行，希望能对其他学习灰色关联度分析的人员有用。-This code is I do gray correlation analysis used matlab code, the experimental data are used, can be run directly, hoping to be useful for other learning gray correlation analysis of s
demo3
- 在demo中，用EKF和有噪声的EKF训练非线性、非平稳数据。-In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance