FIRST_temp

说明：  此代码为一种改进的新型的遗传算法代码，该代码主要应用于优化单目标和优化多目标。(This code is a new improved genetic algorithm code, which is mainly used to optimize single objective and multi-objective.)

人脸识别中的图像预处理期刊，基于matlab的实现的。(Face Recognition in the image preprocessing process, based on the realization of matlab.)

matlab 经典算法程序 包括一些基本的matlab的算法(classical algorithm matlab program includes some basic algorithm matlab)

matlab做的论文资料，从CNKI上找来的，这里免费给大家，希望对大家有用(MATLAB based on the double inverted pendulum control system design method, which died on the CNKI papers, fees, free of charge to everyone here, in the hope that useful)

the most chatoc attractors for encryption and other application

精通MATLAB最优化计算的书籍，精通matlab必读之书(The most proficient in MATLAB Optimization)

传递函数，空间状态方程，z变换，模糊滑模变结构控制(Transfer function, state space equation, z transform, fuzzy variable structure control)

有限脉冲神经网络MATLAB源码，由波兰国立大学生物医药信号处理实验室提供。FIR 神经网络属于一种BP神经网络。(Finite Impulse Response (FIR) neural network models each synapse as a linear filter to provide dynamic interconnectivity. Temporal backpropagation is used to train the network in which error terms are symmetrically filtered backward through the network)

说明：  极点配置直接自校正控制 （最小相位确定性系统）(Pole placement direct self-tuning control (minimum phase deterministic system))

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)