PV_MAX

严格按照BP网络计算公式来设计的一个matlab程序,对BP网络进行了优化设计 优化1：设计了yyy，即在o(k)计算公式时，当网络进入平坦区时(<0.0001)学习率加大， 出来后学习率又还原 优化2：v(i,j)=v(i,j)+deltv(i,j)+a*dv(i,j) (A Matlab procedures in strict accordance with the BP network computing formula to design, the optimization design was carried out on BP network Optimization design of 1: YYY, that is O (k) in the formula, when the network into the flat area (<0.0001) learning rate increase, after learning rate and reduction Optimization of 2:v (I, J) =v (I, J)+deltv (I, J)+a*dv (I, J) )

基于改进差分演化算法的石油层评价指标的特征选择(Feature improved oil layer evaluation based on differential evolution algorithm choice)

研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度 函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了 基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不 仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知 质量。 (Recent research indicates that the speech spectral amplitude distributions could be fairly described with super-Gaussian probability density function. However, the complexity of speech signal determines that the distribution statistics ofspeech signal could not be well described by single simple function. Thus a super-Gaussian mixture model for speech spectral amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech signals spectral amplitude is derived. The simulation results show that this algorithm based on Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech distortion as compared with the conventional short-time spectral amplitude estimation algorithm. )

SQNR的数字信号系统的全仿真，在matlab下可以直接运行。(The simulation of digital signal system of SQNR, can be run directly under matlab.)

说明：  运用遗传算法对BP神经网络输入自变量进行降维(Dimension Reduction of Input Independent Variables of BP Neural Network Using Genetic Algorithms)

Inspect an object handle

实现定时控制，非常实用，可以直接调用，有需要的可以下载(Achieve timing control, very practical, you can directly call the need to be downloaded)

Threshold Adaptive Threshold

说明：  求解单自由度振动 可以直接输入参数计算 简单方便(It is simple and convenient to input parameters directly for solving single degree of freedom vibration)

显示采样率的GUI源码，包括理想采样，零阶保持采样，和其他一些方式。对于学习和理解采样定理很有帮助(Show the sampling rate of the GUI source code, including the ideal sampling, zero-order to keep sampling, and some other way. For learning and understanding of the sampling theorem helpful)