solver_Eq

DVB_TCOFDM接收系统中关键技术研究与设计(DVB_TCOFDM receiver algorithm)

To design a Band Stop Filter using Blackman window matlab code with example

本文基于BP神经网络应用于预测的原理，提出预测步骤及预测可行性，探讨建立基于BP神经网络的预测模型的关键技术，包括样本的选取与预处理、输入输出变量的选取、隐层节点数的确定、初始权值和阈值的选取、激活函数、训练算法与参数的选取，最后建立合理的网络模型；结合住宅市场的实际情况，建立两类BP 神经网络预测模型：基于时间序列的趋势预测模型以及基于影响因素的回归预测模型，即分别采用神经网络趋势预测和回归预测的思路，把住宅市场的供给、需求与房价的历年数据以及其影响因素的数据分别作为学习样本，建立预测模型，进行预测。(Real estate development risk prediction )

为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识 别。对同一负荷下的已知故障信号进行变分模态分解，利用 奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法 应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value decomposition was proposed for fault diagnosis using standard fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were decomposed by VMD, and the modes characteristics were further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the test samples were clustered by a Hamming nearness approach, and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under variable loads. By comparing with a method based on EMD, this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)

this package provides ehe fuzzy c means segmentation algorithm and its input type.. source code is also availble

continous ga codes codes with continous value

kriging代理模型 进行近似计算的 可以自行改变参数(kriging surrogate model to approximate calculation can change the parameters)

Matlab在图像处理与目标识别 这个是对细胞的识别(Matlab image processing and target recognition this is the identification of cells)

针对LDPC码在理论和实现两方面分别进行了较深的研究。深入了解了LDPC码编码方法、译码方法以及性能等知识，重点研究了用MATLAB如何构造奇偶校验矩阵、如何编码和如何解码的算法，而且实现了在相同码率、信噪比下LDPC码性能与卷积码性能的对比。(LDPC codes for both theory and implementation were carried out deeper study. In-depth understanding of the LDPC coding method, decoding method and performance of such knowledge, focuses on how to use MATLAB parity check matrix structure, how to encode and decode algorithms how, but also achieved at the same rate, SNR LDPC codes Comparative performance and convolution code performance.)

To Calculate the Spectral efficiency of a continuous-time AWGN Channel in bits/s/Hz