compilemexface

matlab编写的适用于数值分析求解线性方程组的程序(matlab numerical analysis prepared for the procedure for solving linear equations)

matpower在电力系统最优潮流实时电价中的应用(the use of matpowe in power changes.)

仿真未编码和进行Hamming编码的QPSK调制通过AWGN信道后的误比特率性能(Hamming coding and for the simulation are not encoded by QPSK modulation in AWGN channel, the bit error rate performance)

使用Matlab工具实现哈夫曼压缩编、解码，已执行验证可实现(Using the Matlab tool Huffman compression coding, decoding, validation is performed to achieve)

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 .)

elshebri source code5

Quantization source code

there is a matlab cod that describe how we can compute poincare map in general for nonlinear differential equation,in dynamical system.

关于matlab的编程，主要是plot绘图方面的，请查收！(Matlab programming on the main plot mapping, please check!)

说明：  MATLAB中应用的混沌时间序列分析的1.0版本(Application of MATLAB in the analysis of chaotic time series version 1.0)