Dijkstra

介绍MATLAB的基本操作及MATLAB程序语法等(Describes the basic operation of MATLAB and the MATLAB program gramma and some other skills.)

说明：  通过利用Weichselberger模型产生瑞利衰落信道(rayleigh)

这种方法的痕迹，并构建一个表面，是本地垂直到轴突纤维 (This method traces and construct a surface,that is locally perpendicular to a axonal fiber )

说明：  在matlab环境下用智能算法--人工鱼群算法求函数极值的matlab程序(In the matlab environment with intelligent algorithm- artificial fish-swarm algorithm for the extremal function matlab program)

ica 的基础性文献， 对理解ica原理 及发展很有参考价值。(ica of the basic literature, theory and development in understanding ica useful reference.)

一个神经网络PID控制例子 2C控制对象为一个直升机俯仰轴的角度控制 2C数学模型为二阶传递函数(PID control of a neural network control 2C an example of a helicopter for the angle of pitch-axis control 2C mathematical model for the second-order transfer function)

this algoritm is writen for some adaptive algoritms such lms and vlms algoritm o achive the adaptives and noise canslations

用最优控制原理来控制五自由度无轴承电机（竖立，转子的上、下部各放置四个电磁铁，z轴为竖立方向的控制先不考虑，只考虑，x，y轴方向的稳定)(Using optimal control theory to control the five degrees of freedom bearingless motor (erected on the rotor, the lower part of the placement of four electromagnets, z-axis direction control for the erection of the first not considered, only the, x, y-axis direction of the stable))

Hammerstein_Wiener模型最小二乘向量机辨识及其应用 EI文章(Hammerstein_Wiener model identification and application of least squares vector machines EI article)

产生右图所示图像f1(m,n)，其中图像大小为256×256，中间亮条为128 ×32，暗处=0，亮处=100。对其进行FFT： ① 同屏显示原图f1(m,n)和FFT(f1)的幅度谱图； ② 若令f2(m,n)=（-1）m+n f1(m,n)，重复以上过程，比较二者幅度 谱的异同，简述理由； ③ 若将f2(m,n)顺时针旋转90 度得到f3(m,n)，试显示FFT(f3)的幅 度谱，并与FFT(f2)的幅度谱进行比较； ④ 若将f1(m,n) 顺时针旋转90 度得到f4(m,n)，令f5(m,n)=f1(m,n)+f4(m,n)，试显 示FFT(f5)的幅度谱，并指出其与FFT(f1)和FFT(f4)的关系； ⑤ 若令f6(m,n)=f2(m,n)+f3(m,n)，试显示FFT(f6)的幅度谱，并指出其与FFT(f2)和 FFT(f3)的关系，比较FFT(f6)和FFT(f5)的幅度谱。(Generating an image f1 (m, n) shown in the figure, wherein the image size is 256 256, the intermediate light bar 128 32 0 = dark, bright Department 100. Its FFT: ① screen display picture f1 (m, n) and the FFT (f1) of the amplitude spectrum ② If so f2 (m, n) = (-1) m+n f1 (m, n), repeat The above process, comparing the amplitude spectrum of the similarities and differences between the two, brief reasons ③ If f2 (m, n) 90 degrees clockwise to get f3 (m, n), try to display FFT (f3) the amplitude spectrum and with the FFT (f2) comparing the amplitude spectrum ④ If f1 (m, n) obtained by 90 degrees clockwise f4 (m, n), so f5 (m, n) = f1 (m, n)+f4 (m, n ), try to display FFT (f5) amplitude spectrum, and pointed out its relationship with the FFT (f1) and FFT (f4) of ⑤ If so f6 (m, n) = f2 (m, n)+f3 (m, n) and try to display the FFT (f6) amplitude spectrum, and pointed out its relationship with the FFT (f2) and FFT (f3), comparing FFT (f6) and FFT (f5) amplitude spectrum.)