BFSK.m

基于matlab的蚁群算法智能优化源代码描述的m文件(PSO in MATLAB)

地铁火灾疏散模型 元细胞自动机 神经网络 BP 蚁群模型鱼群模型(Subway fire evacuation model meta-cellular automata neural network BP ant colony model fish colony model)

瑞利衰落信道的误码率性能的matlab的m文件(BER performance for Rayleigh fading channel)

说明：  本书是美国经典数字【信号与处理】的中文版。对于学习通信和信工来说非常有帮助(This book is the American classic digital signal processing】 【Chinese version. Work for learning communication and trust is very helpful)

initialisation de la fcm

Matlab粒子群工具箱，实现优化计算中的粒子群算法功能(The Matlab Particle Swarm toolbox, particle swarm optimization calculation algorithm function)

Setup and run EPANET simulations in Matlab

function dg=gplus(x,y,z,x1,x2,y1,y2,z1,z2,p) G=6.67e-11 t222=myfun(x,y,z,x2,y2,z2) t122=myfun(x,y,z,x1,y2,z2) t212=myfun(x,y,z,x2,y1,z2) t221=myfun(x,y,z,x2,y2,z1) t211=myfun(x,y,z,x2,y1,z1) t121=myfun(x,y,z,x1,y2,z1) t112=myfun(x,y,z,x1,y1,z2) t111=myfun(x,y,z,x1,y1,z1) dg=-G*p*1e6*(t222-t122-t212-t221+t211+t121+t112-t111) 转化为g.u. function y=myfun(x,y,z,xi,eta,zeta) R=sqrt((xi-x).^2+(eta-y).^2+(zeta-z).^2) temp1=(xi-x).*log((eta-y)+R) temp2=(eta-y).*log((xi-x)+R) temp3=(zeta-z).*atan((zeta-z).*R/((xi-x).*(eta-y))) 书中所给公式 temp3=-(zeta-z).*atan((xi-x).*(eta-y)./(zeta-z)./R) 上式变换 y=temp1+temp2+temp3 (function dg = gplus (x, y, z, x1, x2, y1, y2, z1, z2, p) G = 6.67e-11 t222 = myfun (x, y, z, x2, y2, z2) t122 = myfun (x, y, z, x1, y2, z2) t212 = myfun (x, y, z, x2, y1, z2) t221 = myfun (x, y, z, x2, y2, z1) t211 = myfun (x, y, z, x2, y1, z1) t121 = myfun (x, y, z, x1, y2, z1) t112 = myfun (x, y, z, x1, y1, z2) t111 = myfun (x, y, z, x1, y1, z1) dg =-G* p* 1e6* (t222-t122-t212-t221+t211+t121+t112-t111) converted the Gu function y = myfun, (x y, z, xi, eta, zeta) R = sqrt ((xi-x). ^ 2+ (eta-y). ^ 2+ (zeta-z). ^ 2) temp1 = (xi-x).* log ((eta-y)+R) temp2 = (eta-y).* log ((xi-x)+R) temp3 = (zeta-z).* atan ((zeta-z).* R/( (xi-x).* (eta-y))) book given formula Temp3 =- (Zeta-z)* ATAN ((Xi-X)* (ETA-y)/(Zeta- Z)/R) the above formula transform Y = temp1+temp2+temp3 )

针对稀疏圆阵的波达方向估计问题，提出了解相干求根MUSIC算法（Sparse UCA Decorrelation Root-MUSIC，SDR）。通过改进传统的波束变换方法，进行相位校正，并在波束域进行误差补偿，得到具有共轭对称结构的波束域导向矢量。在波束域进行前后向平均处理和使用求根MUSIC算法(In order to solve the direction of arrival estimation of sparse circular arrays, a new understanding of the Sparse UCA Decorrelation Root-MUSIC (SDR) algorithm is proposed. By improving the traditional beam transform method, phase correction, and error compensation in the beam domain, a beam-domain steering vector with a conjugate symmetry structure is obtained. Averaging forward and backward in the beam domain and using the root-finding MUSIC algorithm)

介绍自动对焦的文章主要介绍了对焦技术探测技术包括频域分析目标函数目标函数效能验证影像评估效能之因素高斯低通滤波器参数的选取自动对焦技术以及相关实验结果与分析(Introduction Auto focus of the article introduces the focus technology, detection technology including frequency domain analysis, the objective function, the objective function performance verification, image assessment of the effectiveness of the factors, Gaussian low-pass filter parameter selection, auto-focus technology and related experimental results and analysis.)