Interiorpointmethod

guassian mixture back ground moble matlab code

there is a matlab cod,that is, Example of Supercritical Hopf bifurcation.

Matlab code for audio coding

有限单元法及其应用 彭细荣，matlab在有限单元中的应用及其代码，应用其掌握该方法的弹性求解过程。 (matlab application finite element method )

A GUI matlab model for getting the ionex value

SAR图像压缩感知 matlab代码 对应着汕大闫老师的那本压缩感知及其应用，有需要的可以看一下！！(SAR image compression matlab code that corresponds to the perception that this compressed sensing and its application Shantou Yan teacher in need can look at! !)

机械手的自适应滑模控制matlab仿真程序，完整高效运行(Self-adaptive sliding mode control matlab simulation program, complete and efficient operation)

说明：  可以修改文字的颜色、大小、字体。还可以对文字的方格和粗细进行设定。(You can modify the text color, size, font. The text can also be configured grid and thickness.)

说明：  基于HSM（harominic/stochastic model）模型的声音转换工具包。内含声音的预处理，分割，建模，参数转换以及实验部分。需要具备一定的语音转换相关知识。(Based on HSM (harominic/stochastic model) model voice conversion kit. Includes pre-sound, segmentation, modeling, parameter conversion and experimental parts. Need to have some knowledge of voice conversion.)

Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固定的。在这种网络中, 除了普通的隐含层外, 还有一个特别的隐含层,称为关联层 (或联系单元层 ) 该层从隐含层接收反馈信号, 每一个隐含层节点都有一个与之对应的关联层节点连接。关联层的作用是通过联接记忆将上一个时刻的隐层状态连同当前时刻的网络输入一起作为隐层的输入, 相当于状态反馈。隐层的传递函数仍为某种非线性函数, 一般为 Sigmoid函数, 输出层为线性函数, 关联层也为线性函数。(Elman neural network: Elman JL Elman network is first proposed for speech processing problems in 1990. It is a typical local regression network (global feed for ward l ocal recurrent). Elman network can be seen as having a forward neural network of local memory unit and local feedback connections. Elman network has a multi-layer multilayer structure similar to the network. Its main structure is a feedforward connection, comprising an input layer, a hidden layer and output layer, which can be connected right learning correction feedback connected by a set of " structural" units, the output value of the memory for the previous time, which connection weights are fixed. In such a network, in addition to ordinary hidden layer, there is a special hidden layer, called an associative layer (or contact layer unit) this layer receives feedback signals the hidden layer, each hidden layer node has an associated layer node corresponding connection. The role of the associated layer is fo)