资源列表
RSNA
- 待辨识对象参数a=[1 -1.5 0.7] b=[1 0.5] 输入采用长度L=400的白噪声序列，输出 ，输入和输出数据均含不相关随机噪声，ρ（k）=1/k。利用上述递推公式，辨识系统参数。-To identify the object parameters a = [1-1.5 0.7] B = [1 0.5] Input the length L = 400 white noise sequence, the output, the input and output data
shijuan
- 很好的学习资料，智能信息处理的相关知识，值得下载-Good learning materials, knowledge of intelligent information processing, it is worth to download
GA2
- 基于遗传算法求解多元函数优化问题，程序理论使用变量数目不限制-Based on genetic algorithm to solve the multi-function optimization problem, the use of the procedural theory does not limit the number of variables
RBF
- 径向基神经网络（RBF网络）的三种学习算法实现：随机选取中心法、自组织选取中心和梯度训练算法-Three radial basis function neural network (RBF) learning algorithm: randomly selected center method, self-organizing selection center and gradient training algorithm
M1--12--Study-of-Major-Issues-and-Their-Impact-on
- Study of Major Issues and Their Impact on DVR System Performance
cmean
- 这个算法的基础是误差平方和准则，为了要得到最优结果，首先要对样本集进行划分，一般的做法是先选择一些代表性的点作为聚类的核心，然后把其余的点按照某种方法分到各类中去。-This algorithm is based on error sum of squares criterion, in order to get the optimal results, first of all to sample set division, the general practice is to choose
GA1threshold
- 针对Bmp图片，根据以最大图像的信息熵为目标，采用遗传算法（源码全）对图像进行单阈值变换，实现对图像的阈值分割。（VC6.0实现）-Bmp picture, according to the target maximum image information entropy, genetic algorithm (source all) single threshold transform image threshold segmentation of images. (VC6.0 achieve
GAPBP
- 用遗传算法优化BP神经网络的初始权值，并用于非线性系统辨识。-BP neural network using genetic algorithms to optimize the initial weights, and for the identification of nonlinear systems.
MLS
- 当信噪比较大时，采用广义最小二乘法可能会出现多个局部收敛点，解决这个问题的方法可用多级最小二乘法，一般来说多级最小二乘法包含三级辨识过程。利用输入输出数据，通过多级最小二乘法，可分别求的辅助模型，过程模型和噪声模型的参数估计值。在高噪声的情况下，多级最小二乘法明显优于广义最小二乘法，其收敛点唯一。-When the signal noise ratio (SNR), and by use of the generalized least squares method may appear mor
Untitled8
- bp神经网络 bp神经网络 -bp NN
chapter41
- 清华大学人工智能课件第4章1，仅供参考学习。内容翔实，方便自学。-Tsinghua University Artificial Intelligence courseware Chapter 4 for reference only learning. Informative, convenient self-study.
chapter42
- 清华大学人工智能课件第四章2，仅供参考学习。内容翔实，方便自学。-Tsinghua University Artificial Intelligence courseware Chapter 2, for reference only learning. Informative, convenient self-study.