文本识别是一个基础但却重要的课题，本算法给出其具体的实现过程。...

Tutorial On Fuzzy Logic

串口服务器KEIL C51原代码,单片机采用STC89C58RD+,网络控制器采用RTL8019,经过三层交换机测试,可以直接应用在产品,串口波特率576-KEIL C51 serial server source code, microcontroller used STC89C58RD+, Network controller using RTL8019, after a three-switch testing, can be directly applied to products, serial baud rate 576

svm_c 分类源码 用于svm的分类编程同svm_nu 相近-svm classification svm_c source for the classification of programming with similar svm_nu

用于神经网络分析与设计-for Neural Network Analysis and Design

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

高性能交流伺服系统及其复合控制策略研究，浙江大学博士论文。 关键词:交流伺服系统，矢量控制，直接转矩控制，复合控制，PID控制，GA，IGA，模糊控制，切换控制，协调控制-High Performance AC Servo System and its Advanced Mixed Practical Control Strategy.Thesis of Zhejiang University. Keywords:AC servo system, Vector Control, Direct Torque Control,Combined Control, PID Control, GA, IGA, Fuzzy Control, Coordinating Control.

A*算法，迷宫寻路，初步解析A*算法如何在迷宫寻路-Astar

人工神经网络算法研究及应用 田景文.包括神经网络、遗传算法、小波网络、模拟退火算法和支持向量机，清晰易懂，非常值得一看。-Artificial Neural Network Algorithm Research and Application田景文. Including neural networks, genetic algorithms, wavelet networks, simulated annealing algorithm and support vector machine, clear and easy to understand, is a must see.

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

这是表达支持向量机一个demo，可以帮助更好的理解svm-This is an expression of support vector machine demo, can help a better understanding of svm