模糊控制与PID控制比较,模糊控制仿真的阶跃响应

贝叶斯分类代码,实现分类-Bayesian classification code, realize Classification

一种运算效率较高的遗传算法-a higher genetic algorithms

《模式分析的核方法》一书中的源代码，其中有该书的基本介绍和talk！

使用模拟退火思想解决图着色（GCP）问题，使用先看说明文件-??用??拟退 火??想 ???鐾 ??派???GCP ??问题 ????用先 ?

本程序模拟细胞的自我繁殖，你会看到一个柔软的细胞诞生在电脑屏幕上，然后随着各个分子的撞击，细胞开始分裂，经过一段时间的演化，这个细胞会在溶液中把自己复制，然后这个过程会继续进行……。这种自我复制是在人工化学的基础上进行，比起原来的基于细胞自动机的自我复制更加形象逼真-the cell simulation of self-reproduction, you will see a soft cell birth on the computer screen, and then with all the percussive elements, cell division, after a period of evolution, the cells in the solution themselves copied, and then the process will continue ... . This self-reproduction of the artificial chemical basis, compared to the original cell-based automatic machine self-replication more vivid

人工神经网络理论在井渠结合灌区地下水动态预报中的应用-wenjian

SMC1602A(16*2)模拟口线接线方式 连接线图: --------------------------------------------------- |LCM-----51 | LCM-----51 | LCM------51 | --------------------------------------------------| |DB0-----P0.0 | DB4-----P0.4 | RW-------P2.5 | |DB1-----P0.1 | DB5-----P0.5 | RS-------P2.6 | |DB2-----P0.2 | DB6-----P0.6 | E--------P2.7 | |DB3-----P0.3 | DB7-----P0.7 | VLCD接1K电阻到GND| --------------------------------------------------- Keyboard接线 PS/2--------51 1 DATA------P3.4 3 GND 4 VCC 5 CLK-------P3.3 接在51的外部中断，触发方式为低电平 本程序源码只供学习参考，不得应用于商业用途，如有需要请联系作者。-SMC1602A (16* 2) analog line port to connect wiring diagram:- | LCM-51 | LCM-51 | LCM 51 | | | DB0-P0.0 | DB4-P0.4 | RW-P2.5 | | DB1-P0.1 | DB5-P0.5 | RS-P2.6 | | DB2-P0.2 | DB6-P0.6 | E P2.7 | | DB3-P0.3 | DB7-P0.7 | VLCD access 1K resistor to GND |-Keyboard Connection PS/2 51 1 DATA P3.4 3 GND 4 VCC 5 CLK-P3.3 access in 51 of the external interrupt trigger mode for low-level source for learning the procedu

以黎曼几何为理论依据，基于S.Amari的修正核函数思想提出了两种新的保角变换，用其对核函数进行数据依赖性改进，进一步提高支持向量机分类器泛化能力。以人工非线性分类问题 为对象进行研究，仿真实验结果表明采用新保角映射可以快速显著地改善分类器泛化性能，而且能大幅度地减少支持向量的数目。-Two novel conformal transformations were proposed based on the Riemannian geometry theory and S.Amari’sidea.And the kernel function was modified by the transformation in a data-dependent way.Our experimental results for theartificial nonlinear data set show that the generalization performance of support vector machines classifier is improvedremarkably and the number of support vectors is decreased greatly.

Neural Networks ，8种神经网络，BP，RBF，CPN等通过VC++来实现了！-Neural Networks, 8 Neuro network, BP, RBF, CPN, such as through VC++ To achieve!

分类算法是数据挖掘中最为重要的技术之通过介绍当前最具有代表性的分类算法,分析并总结了各种算法 -NULL