RBF

本人调试的大作业（源程序模型+论文），主要包括PID控制、模糊控制、神经网络控制、遗传算法优化神经网络控制（使用了遗传工具箱GAOT）对同一系统所作的仿真比较，并加入饱和、死区、时滞等非线性后的响应，具体的分析比较过程论文中写的很详细。-I debug a big operation (source model+ Thesis), including PID control, fuzzy control, neural network control, genetic algorithm optimization of neural network control (using a genetic toolbox GAOT) on the same system simulation by the comparison, and to join saturation, dead zones, such as nonlinear time-delay after the response, the specific analysis of the comparison process paper written in great detail.

神经网络hopfield源码- Nerve network hopfield source code

Matlab遗传算法工具箱函数及应用实例 Matlab遗传算法工具箱函数及应用实例-Matlab genetic algorithm toolbox function and application of genetic algorithm Matlab toolbox function and application example

2005年10月份自写的细菌分割、标号与面积统计程序。带测试图像-in October 2005 since the bacteria was divided, with the label area statistical procedures. With test images

移动机器人运动学mathematica程序-Mobile robot kinematics mathematica program

PCA、LDA人脸检测，压缩包里实现了PCA、LDA、最小距离分类器，人脸检测！-PCA, LDA face detection, compression bag to achieve the PCA, LDA, minimum distance classifier, face detection!

有关RBF的参考文献，希望能够给你带来一定的帮助-RBF-related references, hoping to give you some help

petri网络的演示程序，本程序是计算机网络课程中涉及到的petri网络的演示，输入预先的转换规则，搜索可以击发的状态，设定次数进行状态转换-Petri Network Demonstration Program, the process is computer network courses related to the petri network demonstration, the importation of pre-conversion rules can search the state while working on the creation of a state for the number change

用java编写的bp网络和rbf网络的源程序 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。 -with java bp prepared by the network and the networks of the source rbf JNNT by java language, cross-platform advantages can be. java applet demo version of a more simple to just any machine on the View All can be run without having to install any additional software major. Lovers more convenient remote access through the internet resources.

用MATLAB编写的支持向量机，实现了线性可分与线性不可分的情况，还有非线性支持向量机，里面部分常用的核函数。-Prepared using MATLAB support vector machine, to achieve a linearly separable and linear indivisible, it is still non-linear support vector machine, which part of the kernel function used.