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harris边缘检测技术，输入相关参数即可得到相应的边缘检测结果(harris edge detection)

it is a very essential matlab code.

RNNSIM v.2 package contains a number of m-files for training and evaluation of the random neural network. All functions have been thoroughly tested. After downloading the separate files or the zipped file, make sure that they are stored or extracted in the directory /rnnsimv2 Below an overview of the files contained in this directory along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help <function-name> in the MATLAB command window. Along with the m-files in this directory you will find a manual for the simulator in PDF format (rnnsimv2.pdf). Start by printing this out and read the release notes. Two simple demonstration programs are given to illustrate how most of the functions work.

一个关于通过matlab操作和编辑Word文档的例子(matlab Word)

Test file for reg. Please run the other files.

动态矩阵预测控制DMC算法的matlab程序，单输入单输出的。(Dynamic Matrix Predictive control the DMC algorithm of matlab program, single-input single-output.)

说明：  用于预测航运港口利用率的神经网络模型，基于时间递推。泛化(time chain for port)

cdma based congnitive radio application over power allocation.

双极性不归零码、双极性归零码和差分码的时域及功率谱密度图的绘制(bipolar not NRZ, bipolar NRZ and difference time-domain code and power spectral density Map)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )