贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我...

用的是倒谱,加神经网络~不过没有成功,稳定性很差-using the cepstrum, neural network to increase but not a success, poor stability

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

本程序主要实现了费若算法在信息论及编码中的应用-This procedure mainly realize the fee if the algorithm discussed in the Information Coding

水木清华BBS精华区－－人工智能 涉及了人工智能的多个方面问题-Shuimu Tsinghua BBS essence District-- AI Artificial Intelligence involves a number of aspects

10。《用于最优化的计算智能》，Nirwan Ansali，Edwin Hou着，李军，边肇棋译 清华大学出版社 1999年第一版 本书从讨论组合优化中的基本问题――NP问题入手，系统地讲述了近年来所发展起来的智能最优化的各种技术和方法，其中包括启发式搜索、Hopfield神经网络、模拟退火和随机机、均场退火以及遗传算法等；并在此基础上，通过一些典型的应用问题，如旅行商问题、模式识别中的点模式匹配问题、通信和任务调度等问题进一步阐明以上一些基本方法怎样用来解决这些原来具有NP性质的困难问题。本书是作者在美国新泽西州理工学院多年讲授有关课程的基础上写成的。全书深入浅出，理论联系实际。为帮助学生掌握基本概念，提高学习能动性，各章编写了习题。本书可作为通信、计算机、控制各专业的高年级学生和研究生学习有关课程的教材。它对于广大科研工作者也是一本很有实际价值的参考书。 -10. "Optimal for Computational Intelligence," Nirwan Ansali, with Edwin Hou, Li Jun, Pin Zhaoxing chess translation Tsinghua University Press in 1999 the first book version of combinatorial optimization from the discussion of basic questions-- NP problem start, the system described in recent years to develop the intelligent optimization of the technology and methods, These include heuristic search, Hopfield networks, simulated annealing and random machine, mean field annealing and genetic algorithms; and on this basis and through some typical applications, such as the traveling salesman probl

随机神经网络的源代码，对于研究神经网络的人来说，可能有一定的参考作用-random neural network source code for research neural network people, may have some reference

对多类点进行分类，有图像界面，使用了多种分类算法-Points to the many types of classification, there are graphical interfaces, using a variety of classification algorithms

本工具包主要是为对神经网络有兴趣人士提供的一种方便，灵活的学习和研究软件。 JNNT由java语言写成，具有跨平台的优越性能.java applet的演示版更简单到只需要任何机器上的浏览器就可以运行，无需安装任何大型附加软件。更方便爱好者通过internet远程访问资源。 支持反向传播算法(BP),LBG聚类法和径向基网络(RBF) -This toolkit is for people interested in neural networks has provided a convenient, flexible learning and research software. JNNT by the java language, cross-platform with superior performance. Java applet demo version is more simple to just any machine can run a browser, without installing any large-scale add-on software. Enthusiasts through the internet more convenient remote access to resources. To support the back-propagation algorithm (BP), LBG clustering method and radial basis function network (RBF)

knn算法的实现，框架十分清楚。是经典的东西-KNN algorithm, the framework is very clear. Is a classic East-West

java神经网络工具箱-neural network toolbox