Help matlab interp18/31/2023 ![]() If x_int is a vector containing the value or values of the independent variable at which we wish to estimate the dependent variable, then typing interp1 (x,y, x_int) Figure 7.4-7 A plot of temperature data versus time Suppose that x is a vector containing the independent variable data and that y is a vector containing the dependent variable data. Linear interpolation in MATLAB is obtained with the interp1 and interp2 functions. Later in this section we use polynomial functions to do the interpolation, Using straight lines to connect the data points is the simplest form of interpolation. Another function could be used if we have a good reason to do so. Plotting the data sometimes helps to judge the accuracy of the interpolation. In general, the more closely spaced the data, the more accurate the interpolation. When using interpolation, we must always keep in mind that our results will be approximate and should be used with caution. Of course we have no reason to believe that the temperature follows the straight lines shown in the plot, and our estimate of 64☏ will most likely be incorrect, but it might be close enough to be useful. Linear interpolation is so named because it is equivalent to connecting the data points with a linear function (a straight line). We have just performed linear interpolation on the data to obtain an estimate of the missing data. From the plot wethus estimate the temperature at 8 A.M. If we need to estimate the temperature at 10 A.M., we can read the value from the dashed line that connects the data points at 9 A.M. ![]() 12 noon Temperature (✯) 49 57 71 75Ī plot of this data is shown in Figure 7.4-1 with the data points connected by, dashed lines. are missing for some reason, perhaps because of equipment malfunction. ![]() Suppose we have the following temperature measurements, taken once an hour starting at 7:00 A.M. The data’s standard deviation indicates how much the data is spread around the aggregated point. You can use the methods of Sections 7.1 and 7.2 to aggregate the data by computing its mean. data have been aggregated if necessary, so only one value of y corresponds to a specific value of x. If we average the two results, the resulting data point will be x = 10V,y = 3.2 mA, which is an example of aggregating the data, In this section we assume that the. For example, suppose we apply 10 V to a resistor, and measure 3.1 mA of current. Then, repeating the experiment, suppose We measure 3.3 mA the second time. In other cases there will be several measured values of y for a particular value of x. In some applications the data set will contain only . Suppose that x represents the independent variable in the data (such as the applied voltage in the preceding example), and y represents the dependent variable (such as the resistor current). Such plots, some perhaps using logarithmic axes, often help to discover a functional description of the data. Interpolation and extrapolation are greatly aided by plotting the data. In other cases we might need to estimate the variable’s value outside of the given data range. This process is extrapolation. In some applications we want to estimate a variable’s value between the data points. Another type of paired data represents a profile, such as a road profile (which shows the height of the road along its length). ![]() For example, the paired data might represent a cause and effect, or input-output relationship, such as the current produced in a resistor as a result of an applied voltage, or a time history, such as the temperature of an object as a function of time. All the tutorials are completely free.Engineering problems often require the analysis of data pairs. This website contains more than 200 free tutorials! Every tutorial is accompanied by a YouTube video. If we type “help interp1” we can obtain the following options. If we want to perform some other type of interpolation, we need to specify the fourth argument. The MATLAB function “interp1()” computes interpolated values using the default settings that correspond to linear interpolation. In our case, this vector is called “time_dense”. The third argument is a set of values on the x axis at which we want to compute the interpolated values. In our case, the first two arguments are “time_coarse” and “coarse_function” which are used to define the original function values. The first two arguments are the set of points that define the original function. The MATLAB function “interp1()” is used to interpolate the function values. Plot(time_coarse,coarse_function,'o',time_dense,dense_function_interpolated,'.') % Vq = interp1(X,V,Xq) interpolates to find Vq, the values of the underlying function V=F(X) at the query points Xq.ĭense_function_interpolated = interp1(time_coarse,coarse_function,time_dense) Coarse_function=time_coarse.^2-0.1*time_coarse.^3
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