A-Guide-to-MATLAB-Object-Oriented-Programming---A

And OR Not gate in cellular learning automata

preconditioned gradient algorithm for iterative solution

泊松分布的产生及验证泊松分布的小程序，用置信区间验证是否是泊松分布(Possion sequence generation and judgement)

SC取超级权限源码例程，程序调用API函数实现进入SYSTEM超级权限。(SC to take root authority source routines , the program calls API functions into the SYSTEM super powers .)

Lagrange Interpolation

matlab 教程(matlab tutorial)

说明：  线性卡尔曼滤波，二维状态空间模型一般的卡尔曼滤波(kalman fitering)

根据灰色预测理论，用程序实现预测，效果较好(According to the gray prediction theory, use the program predicts better)

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )

顺便附上几张仿真图 这是状态图 要注意的是：必须把系统状态和kalman滤波器内部预测的状态分开(Incidentally, several simulation map attached to this is the state diagram should be noted that: must be the system state and kalman filter to separate the state of internal forecasts)