遗传算法的VS2003工程，调试通过,An Introduction to Genetic Algorithms for Scientists and Engi...

二进制的基本遗传算法，在VC++环境下编程实现。-binary basic genetic algorithm, the VC environment programming.

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

有关遗传算法的源码-the genetic algorithm source code

这是一个parzen窗口分类算法，希望能给大家的学习有所帮助-This is a Parzen window classification algorithm, we hope that they will be helpful to the learning

svm Main_SVM_One_Class 用于svm分类的 在matlab中运用 -svm classification svm Main_SVM_One_Class for use in matlab

2值图像的扫描线2值聚类算法，可实现2值图像的准确聚类-2 the value of the image scan line 2, the value of clustering algorithm can realize the exact 2-level image clustering

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

greedy em 混和模型训练算法-greedy em hybrid model training algorithm

书籍“Regularization tools for training large feed-forward neural networks using Automatic Differentiation”的源码文件-Books

遗传算法分层遗传算法-GA GA stratified