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有什么做设计接任务的网站,温州网站建设技术托管,网站建设大型企业,资金盘网站开发多少钱书籍#xff1a;Matlab实用教程 工具#xff1a;Matlab2021a 电脑信息#xff1a;Intel Xeon CPU E5-2603 v3 1.60GHz 系统类型#xff1a;64位操作系统#xff0c;基于X64的处理器 windows10 专业版 第2章 MATLAB数值计算 2.5 元胞数组和结构数组 2.5.1 元胞数组 1、元胞…书籍Matlab实用教程工具Matlab2021a电脑信息Intel® Xeon® CPU E5-2603 v3 1.60GHz系统类型64位操作系统基于X64的处理器 windows10 专业版第2章 MATLAB数值计算2.5 元胞数组和结构数组2.5.1 元胞数组1、元胞数组的创建A{This is the first cell.,[1 2;3 4],eye(3),{Tom,Jane}} A { [1,1] This is the first cell. [1,2] 1 2 3 4 [1,3] Diagonal Matrix 1 0 0 0 1 0 0 0 1 [1,4] { [1,1] Tom [1,2] Jane } } B{1,1}{This is the second cell.}; B{1,2}{53*i}; B{1,3}{[1 2;3 4;5 6]} B { [1,1] { [1,1] This is the second cell. } [1,2] { [1,1] 5 3i } [1,3] { [1,1] 1 2 3 4 5 6 } }2、元胞数组的内容显示B{1,1}{This is the second cell.}; B{1,2}{53*i}; B{1,3}{[1 2;3 4;5 6]}; celldisp(B) B B{1}{1} This is the second cell. B{2}{1} 5 3i B{3}{1} 1 2 3 4 5 6 B { [1,1] { [1,1] This is the second cell. } [1,2] { [1,1] 5 3i } [1,3] { [1,1] 1 2 3 4 5 6 } } B{1,1}{This is the second cell.}; B{1,2}{53*i}; B{1,3}{[1 2;3 4;5 6]}; cellplot(B)3、元胞数组的内容获取B{1,1}{This is the second cell.}; B{1,2}{53*i}; B{1,3}{[1 2;3 4;5 6]}; x1B{1,2} x2B{1,3} [x3,x4,x5]deal(B{[1 2 3]}) x1 { [1,1] 5 3i } x2 { [1,1] 1 2 3 4 5 6 } x3 { [1,1] This is the second cell. } x4 { [1,1] 5 3i } x5 { [1,1] 1 2 3 4 5 6 }2.5.2 结构数组1、结构数组的创建ps(1).namecurve1 ps(1).colorred ps(1).position[0,0,300,300] ps scalar structure containing the fields: name curve1 ps scalar structure containing the fields: name curve1 color red ps scalar structure containing the fields: name curve1 color red position 0 0 300 300 ps(2)struct(name,curve2,color,blue,position,[100,100,300,300]) ps 1x2 struct array containing the fields: name color position2、结构数组数据的获取和设置ps(2)struct(name,curve2,color,blue,position,[100,100,300,300]) x1ps(2) x2ps(2).position x3ps(2).position(1,3) x4getfield(ps,{2},color) x5getfield(ps,{2},color,{1}) pssetfield(ps,{2},color,green); ps(2) ps 1x2 struct array containing the fields: name color position x1 scalar structure containing the fields: name curve2 color blue position 100 100 300 300 x2 100 100 300 300 x3 300 x4 blue x5 b ans scalar structure containing the fields: name curve2 color green position 100 100 300 3003、结构数组域的获取ps(1)struct(name,curve1,color,red,position,[0,0,300,300]) ps(2)struct(name,curve2,color,blue,position,[100,100,300,300]) x6fieldnames(ps) all_x[ps.name] cat(1,ps.position) cat(2,ps.position) cat(3,ps.position) ps scalar structure containing the fields: name curve1 color red position 0 0 300 300 ps 1x2 struct array containing the fields: name color position x6 { [1,1] name [2,1] color [3,1] position } all_x curve1curve2 ans 0 0 300 300 100 100 300 300 ans 0 0 300 300 100 100 300 300 ans ans(:,:,1) 0 0 300 300 ans(:,:,2) 100 100 300 3002.6 数据分析2.6.1 数据统计和相关分析X[5.30 13.00 0.40;5.10 11.80 -1.70;3.70 8.10 0.60;1.50 7.70 -4.50] max(X) min(X) mean(X) std(X) median(X) var(X) Ccov(X) Scorrcoef(X) [S,k]sort(X,1) X 5.3000 13.0000 0.4000 5.1000 11.8000 -1.7000 3.7000 8.1000 0.6000 1.5000 7.7000 -4.5000 ans 5.3000 13.0000 0.6000 ans 1.5000 7.7000 -4.5000 ans 3.9000 10.1500 -1.3000 ans 1.7512 2.6489 2.3735 ans 4.4000 9.9500 -0.6500 ans 3.0667 7.0167 5.6333 C 3.0667 4.0867 3.0667 4.0867 7.0167 2.7100 3.0667 2.7100 5.6333 S 1.0000 0.8810 0.7378 0.8810 1.0000 0.4310 0.7378 0.4310 1.0000 S 1.5000 7.7000 -4.5000 3.7000 8.1000 -1.7000 5.1000 11.8000 0.4000 5.3000 13.0000 0.6000 k 4 4 4 3 3 2 2 2 1 1 1 32.6.2 差分和积分A[5.30 13.00 0.40;5.10 11.80 -1.70;3.70 8.10 0.60;1.50 7.70 -4.50] diff(A,1,1) gradient(A) sum(A) cumsum(A,2) cumprod(A,2) trapz(A) cumtrapz(A) A 5.3000 13.0000 0.4000 5.1000 11.8000 -1.7000 3.7000 8.1000 0.6000 1.5000 7.7000 -4.5000 ans -0.2000 -1.2000 -2.1000 -1.4000 -3.7000 2.3000 -2.2000 -0.4000 -5.1000 ans 7.7000 -2.4500 -12.6000 6.7000 -3.4000 -13.5000 4.4000 -1.5500 -7.5000 6.2000 -3.0000 -12.2000 ans 15.6000 40.6000 -5.2000 ans 5.3000 18.3000 18.7000 5.1000 16.9000 15.2000 3.7000 11.8000 12.4000 1.5000 9.2000 4.7000 ans 5.3000 68.9000 27.5600 5.1000 60.1800 -102.3060 3.7000 29.9700 17.9820 1.5000 11.5500 -51.9750 ans 12.2000 30.2500 -3.1500 ans 0 0 0 5.2000 12.4000 -0.6500 9.6000 22.3500 -1.2000 12.2000 30.2500 -3.1500 t0:0.5:10; yexp(-0.2).*sin(t) d[0 diff(y)] s10.5*cumsum(y) s2cumtrapz(t,y) y Columns 1 through 8: 0 0.3925 0.6889 0.8167 0.7445 0.4900 0.1155 -0.2872 Columns 9 through 16: -0.6196 -0.8003 -0.7851 -0.5776 -0.2288 0.1761 0.5379 0.7680 Columns 17 through 21: 0.8100 0.6537 0.3374 -0.0615 -0.4454 d Columns 1 through 8: 0 0.3925 0.2964 0.1277 -0.0722 -0.2545 -0.3744 -0.4027 Columns 9 through 16: -0.3324 -0.1807 0.0152 0.2075 0.3489 0.4049 0.3618 0.2301 Columns 17 through 21: 0.0420 -0.1563 -0.3163 -0.3989 -0.3839 s1 Columns 1 through 8: 0 0.1963 0.5407 0.9491 1.3213 1.5663 1.6241 1.4805 Columns 9 through 16: 1.1707 0.7705 0.3779 0.0891 -0.0253 0.0628 0.3317 0.7157 Columns 17 through 21: 1.1207 1.4476 1.6163 1.5856 1.3629 s2 Columns 1 through 8: 0 0.0981 0.3685 0.7449 1.1352 1.4438 1.5952 1.5523 Columns 9 through 16: 1.3256 0.9706 0.5742 0.2335 0.0319 0.0188 0.1973 0.5237 Columns 17 through 21: 0.9182 1.2842 1.5320 1.6009 1.47422.6.3 卷积和快速傅里叶变换Aones(1,10); A(1)0 Bones(1,9); B([1 2 3])0 Cconv(A,B) N32; AFfft(A,N) BFfft(B,N) CFAF.*BF CCreal(ifft(CF)) A 0 1 1 1 1 1 1 1 1 1 B 0 0 0 1 1 1 1 1 1 C 0 0 0 0 1 2 3 4 5 6 6 6 6 5 4 3 2 1 AF Columns 1 through 4: 9.0000 0i 4.3815 - 6.5574i -1.9239 - 4.6447i -1.5927 - 0.3168i Columns 5 through 8: 0.7071 - 0.7071i -0.3960 - 1.9910i -1.3827 - 0.5727i -0.1285 0.0858i Columns 9 through 12: 0 - 1.0000i -1.0704 - 0.7152i -0.6173 0.2557i 0.0642 - 0.3228i Columns 13 through 16: -0.7071 - 0.7071i -0.9039 0.1798i -0.0761 0.1838i -0.3542 - 0.5300i Columns 17 through 20: -1.0000 0i -0.3542 0.5300i -0.0761 - 0.1838i -0.9039 - 0.1798i Columns 21 through 24: -0.7071 0.7071i 0.0642 0.3228i -0.6173 - 0.2557i -1.0704 0.7152i Columns 25 through 28: 0 1.0000i -0.1285 - 0.0858i -1.3827 0.5727i -0.3960 1.9910i Columns 29 through 32: 0.7071 0.7071i -1.5927 0.3168i -1.9239 4.6447i 4.3815 6.5574i BF Columns 1 through 4: 6.0000 0i 2.6719 - 4.9988i -2.6310 - 3.9375i -3.3624 0.3312i Columns 5 through 8: -0.7071 1.7071i 0.2625 0.3199i -0.6756 0.1344i -0.3805 1.2542i Columns 9 through 12: 1.0000 1.0000i 1.0293 - 0.3122i 0.0898 - 0.4514i 0.1710 0.1403i Columns 13 through 16: 0.7071 - 0.2929i 0.1005 - 1.0200i -0.7832 - 0.5233i -0.4923 0.2632i Columns 17 through 20: 0 0i -0.4923 - 0.2632i -0.7832 0.5233i 0.1005 1.0200i Columns 21 through 24: 0.7071 0.2929i 0.1710 - 0.1403i 0.0898 0.4514i 1.0293 0.3122i Columns 25 through 28: 1.0000 - 1.0000i -0.3805 - 1.2542i -0.6756 - 0.1344i 0.2625 - 0.3199i Columns 29 through 32: -0.7071 - 1.7071i -3.3624 - 0.3312i -2.6310 3.9375i 2.6719 4.9988i CF Columns 1 through 3: 54.0000 0i -21.0721 - 39.4230i -13.2269 19.7954i Columns 4 through 6: 5.4603 0.5378i 0.7071 1.7071i 0.5330 - 0.6494i Columns 7 through 9: 1.0111 0.2011i -0.0588 - 0.1938i 1.0000 - 1.0000i Columns 10 through 12: -1.3251 - 0.4020i 0.0600 0.3016i 0.0563 - 0.0462i Columns 13 through 15: -0.7071 - 0.2929i 0.0926 0.9400i 0.1558 - 0.1041i Columns 16 through 18: 0.3138 0.1678i 0 0i 0.3138 - 0.1678i Columns 19 through 21: 0.1558 0.1041i 0.0926 - 0.9400i -0.7071 0.2929i Columns 22 through 24: 0.0563 0.0462i 0.0600 - 0.3016i -1.3251 0.4020i Columns 25 through 27: 1.0000 1.0000i -0.0588 0.1938i 1.0111 - 0.2011i Columns 28 through 30: 0.5330 0.6494i 0.7071 - 1.7071i 5.4603 - 0.5378i Columns 31 and 32: -13.2269 - 19.7954i -21.0721 39.4230i CC Columns 1 through 8: 0.0000 0.0000 0.0000 0.0000 1.0000 2.0000 3.0000 4.0000 Columns 9 through 16: 5.0000 6.0000 6.0000 6.0000 6.0000 5.0000 4.0000 3.0000 Columns 17 through 24: 2.0000 1.0000 0.0000 0.0000 0.0000 0 0.0000 0.0000 Columns 25 through 32: -0.0000 0.0000 0.0000 0.0000 -0.0000 0.0000 0.0000 0.00002.6.4 向量函数a[1 2 3]; b[4 5 6]; ccross(a,b) ddot(a,b) c -3 6 -3 d 32