mimo_simulator

Simulink model for closed step change in inverter.

MATLAB 数学工具软件 实例简明教程(MATLAB mathematical tools simple tutorial examples)

matlab7的学习资料，清华出的书，一本很基础的资料，特别适合初学者(matlab7 learning materials, the university out of the book, a very basic information, especially for beginners)

IIR滤波器的设计，脉冲响应不变法和双线性法都有。 巴斯沃斯滤波器 椭圆滤波器 切比雪夫滤波器(The design of IIR filters, the impulse response have the same method and the bilinear method. Bath Butterworth filter elliptic filter Chebyshev filter)

模拟退火遗传算法通用代码,可以自己更改哈！(General simulated annealing genetic algorithm code, you can change yourself Ha!)

QPSK高斯噪声信道下的星座点和比特误码率仿真曲线(QPSK constellations point under gaussian noise channel and the bit error rate curve simulation)

Hook-Jeeves法解无约束最优化问题(Hook-Jeeves Solving unconstrained optimization problem)

合成孔径雷达成像算法仿真，即一些雷达仿真算法实现(the synthetic aperture radar imaging algorithms)

误码率仿真原理推导睡眠以及相应的matlab程序(BER simulation schematic derivation of sleep and the corresponding matlab program)

用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。(Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared.)