用于油藏数值模拟中的非均值场，即可以改变渗透率变异系数，渗透率级差，可用于eclipse、VIP、cmg中皆可使用，本程序的例子是共有7个层，所以输入数据有7个...

一种RISC结构8位微控制器的设计与实现-The structure of a RISC micro-controller" s 8 Design and Implementation

字符串读写函数fgets和fputs ，读字符串函数fgets函数的功能1-string literacy fgets and fputs function, reading the string functions fgets of functionality 1

订票系统，c语言，七大功能模块，订票，查票，退票等等，无数据库连接，相应数据导入flight.txt文件

此程序完成N皇后问题。大家可以互相讨论学习。-N Queen"s problem. We can learn from each other discussions.

档案资料管理系统-file management system

现在广被认可的ｎｃｉｅ考试的有关试题！ 有兴趣的可以看看！-now widely recognized ncie examination of the papers! Interested parties can see!

You need to download the general purpose toolbox and the signal toolbox. You need to unzip these toolboxes in your working directory, so that you have toolbox_general/ and toolbox_signal/ in your directory. -You need to download the general purpose toolbox and the signal toolbox. You need to unzip these toolboxes in your working directory, so that you have toolbox_general/ and toolbox_signal/ in your directory.

摘得 有序样品聚类-Won orderly clustering method

Visual C++课程设计案例精编--通讯录-Visual C cases intensive curriculum design series-- directory

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5―2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。-The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network,