matlab常用命令

一、MATLAB常用的基本数学函数abs(x)：纯量的绝对值或向量的长度angle(z)：复数z的相角(Phase angle)sqrt(x)：开平方real(z)：复数z的实部imag(z)：复数z的虚部conj(z)：复数z的共轭复数round(x)：四舍五入至最近整数fix(x)：无论正负，舍去小数至最近整数floor(x)：地板函数，即舍去正小数至最近整数ceil(x)：天花板函数，即加入正小数至最近整数rat(x)：将实数x化为分数表示rats(x)：将实数x化为多项分数展开sign(x)：符号函数 (Signum function)。

当x<0时，sign(x)=-1；当x=0时，sign(x)=0;当x>0时，sign(x)=1。

rem(x，y)：求x除以y的馀数gcd(x，y)：整数x和y的最大公因数lcm(x，y)：整数x和y的最小公倍数exp(x)：自然指数pow2(x)：2的指数log(x)：以e为底的对数，即自然对数或log2(x)：以2为底的对数log10(x)：以10为底的对数二、MATLAB常用的三角函数sin(x)：正弦函数cos(x)：馀弦函数tan(x)：正切函数asin(x)：反正弦函数acos(x)：反馀弦函数atan(x)：反正切函数atan2(x，y)：四象限的反正切函数sinh(x)：超越正弦函数cosh(x)：超越馀弦函数tanh(x)：超越正切函数asinh(x)：反超越正弦函数acosh(x)：反超越馀弦函数atanh(x)：反超越正切函数三、适用於向量的常用函数有：min(x): 向量x的元素的最小值max(x): 向量x的元素的最大值mean(x): 向量x的元素的平均值median(x): 向量x的元素的中位数std(x): 向量x的元素的标准差diff(x): 向量x的相邻元素的差sort(x): 对向量x的元素进行排序（Sorting）length(x): 向量x的元素个数norm(x): 向量x的欧氏（Euclidean）长度sum(x): 向量x的元素总和prod(x): 向量x的元素总乘积cumsum(x): 向量x的累计元素总和cumprod(x): 向量x的累计元素总乘积dot(x， y): 向量x和y的内积cross(x， y): 向量x和y的外积四、MATLAB的永久常数i或j：基本虚数单位（即）eps：系统的浮点（Floating-point）精确度inf：无限大，例如1/0nan或NaN：非数值（Not a number），例如0/0 pi：圆周率p（= 3.1415926...）realmax：系统所能表示的最大数值realmin：系统所能表示的最小数值nargin: 函数的输入引数个数nargin: 函数的输出引数个数五、MATLAB基本绘图函数plot: x轴和y轴均为线性刻度（Linear scale）loglog: x轴和y轴均为对数刻度（Logarithmic scale）semilogx: x轴为对数刻度，y轴为线性刻度semilogy: x轴为线性刻度，y轴为对数刻度六、plot绘图函数的叁数字元颜色字元图线型态y 黄色 . 点k 黑色 o 圆w 白色 x xb 蓝色 + +g 绿色 * *r 红色 - 实线c 亮青色 : 点线m 锰紫色 -. 点虚线-- 虚线七、注解xlabel('Input Value'); % x轴注解ylabel('Function Value'); % y轴注解title('Two Trigonometric Functions'); % 图形标题legend('y = sin(x)'，'y = cos(x)'); % 图形注解grid on; % 显示格线八、二维绘图函数bar 长条图errorbar 图形加上误差范围fplot 较精确的函数图形polar 极座标图hist 累计图rose 极座标累计图stairs 阶梯图stem 针状图fill 实心图feather 羽毛图compass 罗盘图quiver 向量场图=========================help network/sim.mSIM Simulate a neural network.Syntax[Y，Pf，Af，E，perf] = sim(net，P，Pi，Ai，T)[Y，Pf，Af，E，perf] = sim(net，{Q TS}，Pi，Ai，T)[Y，Pf，Af，E，perf] = sim(net，Q，Pi，Ai，T)DescriptionSIM simulates neural networks.[Y，Pf，Af，E，perf] = SIM(net，P，Pi，Ai，T) takes，NET - Network.P - Network inputs.Pi - Initial input delay conditions， default = zeros.Ai - Initial layer delay conditions，default = zeros.T - Network targets，default = zeros.and returns:Y - Network outputs.Pf - Final input delay conditions.Af - Final layer delay conditions.E - Network errors.perf - Network performance.Note that arguments Pi， Ai， Pf，and Af are optional and need only be used for networks that have input or layer delays.SIM's signal arguments can have two formats: cell array or matrix.The cell array format is easiest to describe. It is most convenient for networks with multiple inputs and outputs，and allows sequences of inputs to be presented:P - NixTS cell array，each element P{i，ts} is an RixQ matrix.Pi - NixID cell array，each element Pi{i，k} is an RixQ matrix.Ai - NlxLD cell array， each element Ai{i，k} is an SixQ matrix.T - NtxTS cell array，each element P{i，ts} is an VixQ matrix.Y - NOxTS cell array， each element Y{i，ts} is a UixQ matrix.Pf - NixID cell array，each element Pf{i，k} is an RixQ matrix.Af - NlxLD cell array， each element Af{i，k} is an SixQ matrix.E - NtxTS cell array，each element P{i，ts} is an VixQ matrix.Where:Ni = net.numInputsNl = net.numLayers，No = net.numOutputsID = net.numInputDelaysLD = net.numLayerDelaysTS = number of time stepsQ = batch sizeRi = net.inputs{i}.sizeSi = yers{i}.sizeUi = net.outputs{i}.sizeThe columns of Pi， Pf， Ai，and Af are ordered from oldest delaycondition to most recent:Pi{i，k} = input i at time ts=k-ID.Pf{i，k} = input i at time ts=TS+k-ID.Ai{i，k} = layer output i at time ts=k-LD.Af{i，k} = layer output i at time ts=TS+k-LD.The matrix format can be used if only one time step is to be simulated (TS = 1). It is convenient for networks with only one input and output，but can also be used with networks that have more.Each matrix argument is found by storing the elements of the corresponding cell array argument into a single matrix:P - (sum of Ri)xQ matrixPi - (sum of Ri)x(ID*Q) matrix.Ai - (sum of Si)x(LD*Q) matrix.T - (sum of Vi)xQ matrixY - (sum of Ui)xQ matrix.Pf - (sum of Ri)x(ID*Q) matrix.Af - (sum of Si)x(LD*Q) matrix.E - (sum of Vi)xQ matrix[Y，Pf，Af] = SIM(net，{Q TS}，Pi，Ai) is used for networkswhich do not have an input，such as Hopfield networkswhen cell array notation is used.ExamplesHere NEWP is used to create a perceptron layer with a2-element input (with ranges of [0 1])，and a single neuron.net = newp([0 1;0 1]，1);Here the perceptron is simulated for an individual vector，a batch of 3 vectors，and a sequence of 3 vectors.p1 = [.2; .9]; a1 = sim(net，p1)p2 = [.2 .5 .1; .9 .3 .7]; a2 = sim(net，p2)p3 = {[.2; .9] [.5; .3] [.1; .7]}; a3 = sim(net，p3)Here NEWLIND is used to create a linear layer with a 3-elementinput， 2 neurons.net = newlin([0 2;0 2;0 2]，2，[0 1]);Here the linear layer is simulated with a sequence of 2 inputvectors using the default initial input delay conditions (all zeros).p1 = {[2; 0.5; 1] [1; 1.2; 0.1]};[y1，pf] = sim(net，p1)Here the layer is simulated for 3 more vectors using the previousfinal input delay conditions as the new initial delay conditions.p2 = {[0.5; 0.6; 1.8] [1.3; 1.6; 1.1] [0.2; 0.1; 0]};[y2，pf] = sim(net，p2，pf)Here NEWELM is used to create an Elman network with a 1-element input，and a layer 1 with 3 TANSIG neurons followed by a layer 2with 2 PURELIN neurons. Because it is an Elman network it has atap delay line with a delay of 1 going from layer 1 to layer 1.net = newelm([0 1]，[3 2]，{'tansig'，'purelin'});Here the Elman network is simulated for a sequence of 3 valuesusing default initial delay conditions.p1 = {0.2 0.7 0.1};[y1，pf，af] = sim(net，p1)Here the network is simulated for 4 more values， using the previous final delay conditions as the new initial delay conditions.p2 = {0.1 0.9 0.8 0.4};[y2，pf，af] = sim(net，p2，pf，af)AlgorithmSIM uses these properties to simulate a network NET.NET.numInputs， NET.numLayersNET.outputConnect， NET.biasConnectNET.inputConnect， yerConnectThese properties determine the network's weight and bias values，and the number of delays associated with each weight:NET.inputWeights{i，j}.valueyerWeights{i，j}.valueyers{i}.valueNET.inputWeights{i，j}.delaysyerWeights{i，j}.delaysThese function properties indicate how SIM applies weight andbias values to inputs to get each layer's output:NET.inputWeights{i，j}.weightFcnyerWeights{i，j}.weightFcnyers{i}.netInputFcnyers{i}.transferFcnSee Chapter 2 for more information on network simulation.See also INIT， REVERT， ADAPT， TRAIN。