shuzhuang1

数字信号处理中的基于Monte Carlo的方法(Monte Carlo-based methods in signal processing)

本章将介绍 Hitech-PICC 编译器的一些基本概念，由于篇幅所限将不涉及C 语言的标准 语法和基础知识介绍，因为在这些方面都有大量的书籍可以参考。重点突出针对PIC 单片 机的特点而所需要特别注意的地方。(This chapter will introduce the Hitech-PICC compiler of some basic concepts, lack of space will not be involved in the standard C language to introduce basic knowledge of grammar and because in these areas can have a large number of reference books. Focused features for the PIC MCU and the need of special attention.)

贝叶斯分类器 模式识别（电子工业出版社）源代码(Bayesian classifier ..........)

成功实现计算RIpQbI算法的程序，本人测试已经通过，性能已基本达到RIpQbI算法的性能，需要MATLAB 2009b环境运行，如有问题，欢迎讨论 (The successful implementation of the program to calculate RIpQbI algorithm, I have passed the test, need MATLAB2009b operating environment, any questions, please contact me )

利用matlab编写的串行通信助手程序，GUI界面对学通信的应该很有帮助(Matlab serial communication using a helper application written, GUI interface should be helpful in learning to communicate)

Direct Adaptive Control Algorithms：Theory and Application

最大功率点跟踪仿真模型，采用扰动观察法效果理想(Maximum Power Point Tracking Simulation Model)

finl load flow algorithm

离散迭代，对于离散系统，进行多次迭代，得到最终的准确结果(Discrete iteration)

BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.)