neural-work

本程序涵盖预测控制模糊控制等多个知识点运行效果良好(This procedure covers the fuzzy control, predictive control point running well over knowledge)

The Levenberg–Marquardt Method writed by Lourakis

迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡，迫零均衡(zero-forcing)

此代码为本人运用视频读取函数实现视频帧的读取，并可批量转为BMP等格式，另有运动目标静止背景的提取。内附说明文档，欢迎交流。(this code for the use of video I read function video frames to read and can volume to BMP format. another campaign goal from the background static. Enclosing documentation, welcomed the exchange.)

说明：  bp神经网络的预测，烧结式氧化铝的返料成分的在线预测(bp neural network prediction, the online prediction of the return material composition of the sintered alumina)

m_map文件在MATLAB中非常重要，尤其是画与海洋以及地形有关的图中，但缺乏中文资料，这是对其英文的部分翻译(m_map file in MATLAB is very important, especially painting and oceans and terrain-related figure, but the lack of Chinese data, which is part of its English translation)

matlab软件在有限差中的应用，里面含有一篇相关论文(the software matlab finite difference, which contains a related paper)

CT投影数据模拟程序,生成CT的投影数据，十分有用，附说明pdf(Projection simulation)

说明：  自己翻译的一篇最新外文文献《使用无需重建最佳探测器的宽带压缩传感认知无线电》(Own translation of a the latest foreign literature use without having to rebuild the best detectors broadband compressed sensing cognitive radio &quot )

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)