Chaotic-Particle-Swarm-Optimization

Big Bang-Big Crunch algorithm for optimization problems.

这是Simulink关于巴特沃兹滤波器的模型。希望对初学者有用。(This is the Simulink model about bart ward filter. Hope useful for beginners. )

光子晶体方面的国外经典教材,复旦大学光子晶体课程选用教材。(Photonic crystal materials, foreign classics, Fudan University courses use teaching materials photonic crystals.)

神经网络在matlab中的使用说明书 也是新手上手的好东西(Neuron Network Architectures)

城市交通流的运行存在着高度的复杂性、时变性和随机性,实时准确的交通流预测是智能交通系统,特别是先进的交通管理系统与先进的出行者信息系统研究的关键. 基于交通流预测的特点,给出了基于遗传算法的小波神经 网络的交通预测模型GA-WNN ,用具有自然进化规律的遗传算法来对小波神经网络的连接权值和伸缩平移尺度进行前期优化训练,部分代替了小波框架神经网络中按单一梯度方向进行参数优化的梯度下降法,克服了单一梯度下降法易陷入局部极小和引起振荡效应等缺陷. 仿真实验验证了GA-WNN 预测模型对短时交通流的预测的有效性.(For the high complexity ,time-variation and probability of urban traffic flow , its real-time and exact prediction is critical to the research of intelligent traffic systems , especially for the advanced traffic manage-ment system and advanced traveler information system. Based on the character of the traffic flow prediction , a GA-WNN model is given based on the wavelet neural network with genetic algorithm. The genetic algorithm of natural evolving law for the gradient descendent algorithm in Wavelet Neural Network is partly substituted to pre-optimize the connection weight and the extension scale of the wavelet neural network and later optimize the parameters along a single gradient vector. This method overcomes some drawbacks when there exists a single gradient descendent algorithm , such as local minimum and oscillation. A short-time traffic flow prediction sim- ulation using the GA2WNN prediction model demonstrates the validity of the model .)

MATLAB中的基本语法和语句示例代码，MATLAB中基本的知识，具有实用性(MATLAB basic grammar and sentence sample code, MATLAB basic knowledge, practical)

an a dedicated hyperterminal

《数字信号处理：基于计算机的方法（第三版）》资源下载第三部分。 著者： （美）米特拉著 作译者： 孙洪等译 ISBN号： 7-121-02652-X 出版日期： 2006-06 出版社：电子工业出版社 这是本书的精华部分，内有书中从第二章（chap02）到第十一章(chap11)的程序源代码。加上之前本上上传的DEMO文件，对语音，图片，视频信号的数字处理有更好的理解。()

说明：  程序代码说明 P0502：计算图像中的欧拉数(Procedure Code Description P0502: the image of the Euler number calculation)

EXAMPLE9_2_1.M 函數newelm的使用 EXAMPLE9_2_2.M 訓練Elman類神經網路 EXAMPLE9_3_1.M 函數newhop的使用 EXAMPLE9_3_2.M 創建具有兩個神經元的hopfield網路( EXAMPLE9_2_1.M : the use of fuction newelm EXAMPLE9_2_2.M : train Elman neural network EXAMPLE9_3_1.M : the use of fuction newhop EXAMPLE9_3_2.M : to create two neurons of hopfield network)