资源列表
Apriori
- Apriori 算法的实现，数据挖掘的经典算法实现-Apriori algorithm to achieve the classic data mining algorithms to achieve
softmax_exercise-
- ufldl 中 softmax_exercise-ufldl, softmax_exercise
MATLAB-neural-network-30
- 神经网络30案例程序，对神经网络以及SVM学习者有很大的帮助-30 case program, neural network has a great help to learners of neural network and SVM
NSGA-2
- NSGA-2的源程序,经典的多目标优化算法，对于学习多目标优化算法很有帮助。-The source program of NSGA-2，the classical multi-objective optimization algorithms,it is helpful to learning the multi-objective optimization.
arx
- 关于ARX的一阶、二阶、三阶模型的分析、对比。此为完整的程序，稍加修改参数即可。-ARX' s on first order, second order, third-order model of analysis, comparison. This is a complete program, slightly modified parameters.
Isodata
- Isodata聚类算法，出书参数可以设定，参照1986版蔡元龙老师《模式识别》相关内容编写，供初学者学习之用。-Isodata clustering algorithm, the parameters can be set to a book, written with reference to version 1986 Cai Yuanlong teacher pattern recognition related content, for beginners learning.
Kmeans
- 经典Kmeans算法，输入为训练样本和聚类数。-Kmeans classical algorithm, the input for training samples and the number of clusters.
Linear-Regression
- 线性回归的学习算法。包括数据分析、线性回归、在线梯度下降、多项式回归。压缩包中给出.txt数据文件及说明文档。-Linear regression learning algorithm. Including data analysis, linear regression line gradient descent, polynomial regression. Compressed data given .txt file and documentation.
youhuadelizi(matlab)
- 基于matlab的人工神经网络的优化的例子和其仿真程序-Examples of optimization matlab based artificial neural network and its simulation program
Desktop
- 在故障诊断研究中,模式识别环节很重要，该程序是支持向量机模式识别的MATLAB程序。-In the study, fault diagnosis, pattern recognition is very important part of the program is to support vector machine pattern recognition MATLAB program.
Desktop
- 在故障诊断中，对振动信号进行小波分析时候需要用到的小波尺度谱应用程序。-In fault diagnosis, wavelet analysis for vibration signal when the need to use the application spectrum of the wavelet scale.
shenjingwanglouzhongheyingyong
- 神经网络综合应用 主要有BP神经网络的应用、PID神经网络的控制、遗传算法优化神经网络、模糊神经网络、概率神经网络-Neural network integrated applications are mainly controlled BP neural network applications, PID neural networks, genetic algorithm optimization of neural networks, fuzzy neural network, proba