Spline Interpolation Method: This method uses cubic interpolation to find the interpolated values of the given data.Memory allocation and computation time are the same as the pchip Interpolation method. Cubic Interpolation Method: This functions in the same way as defined in the above pchip Interpolation Method.Memory allocation and computation time are more than the Linear Interpolation Method. It requires at least four points to find the interpolated values. Pchip Interpolation Method: The Shape-preserving piecewise cubic interpolation method defines the interpolated values by preserving the shape of the data.It also requires at least 2 points to find the interpolated values. Previous Interpolation Method: This method utilizes the previous element at the sample grid point to find the interpolated values at the query point.Next Interpolation Method: This method utilizes the next element at the sample grid point to find the interpolated values at the query point.Nearest Interpolation method: This method employs the nearest element at the sample grid point to determine the interpolated values at the query point.Computation time and memory allocation are more than the nearest algorithm method. There are certain limitations of this method, like 2 points are at least required to use Linear Interpolation. Linear Interpolation Method: People typically use the default interpolation method. It helps find the interpolated values at the query point, which is based on the values of grid points in each dimension defined.There are various types of interpolation methods in Matlab. To plot the interpolated values without defining the specified points: The accepted data types are double, single, datetime, and duration. The third input value contains all the query points, which can be a vector, matrix, scalar, or array of real numbers. The accepted data type is double, single, datetime, and duration. The second input value, i.e., ‘a’, can be a vector, matrix, or array of complex and real numbers. The supported data types are double, single, datetime, and duration. If a is a vector, the length of x should equal the length of a, while if a is an array, the length of x should equal the size(a,1). The x length depends on another input argument, i.e., ‘a’. The input arguments have specific criteria and rules the first input value, x, should be a vector of only real numbers, and its values should be distinct. To define the sample values of x and a to find the interpolated values using a different interpolated method. To define the sample values of x and a to find the interpolated values: Please find the below examples, which explain the concept of linear interpolation in Matlab: Example #1 If a is an array, then the default set of points lies within a range of 1 to size(a,1). The default set of points lies within a range of 1 to the length of a. The default set of numbers falls under a specific range of 1 to n, where n is decided according to the shape of a. aq=interp1(a, xq): This function will retrieve the interpolated values based on a set of assumed coordinates.We can mention extrapolation to ‘extrap’ if we apply the extrapolation algorithm to the points. aq=interp1(x, a, xq, method, extrapolation method): We can define extrapolation in the syntax to include checking points that are declared outside the defined value of x.The default method used is always linear. There are many interpolation methods like nearest, linear, next, previous, cubic, v5cubic, pchip, spline, or makima. aq=interp1(x, a, xq, method): Here, we can change the interpolation method, which we will discuss later.If there are many values, a can be declared in an array. The input ‘x’ is a vector that contains every sample point, a has the defined values, and xq contains the coordinates. aq=interp1(x, a, xq): This returns the interpolated values of the function (one-dimensional) with the help of the linear interpolation method.Here, we will mainly discuss one-dimensional interpolation or linear interpolation syntax: Various functions accompany interpolation techniques. It is used to find the missing data in the data set, smoothen the given data set or predict the outcome. In Matlab, interpolation is the procedure of including new points within a defined range or a given set of points. Working of Interpolation in Matlab with Syntax and Examples: In this topic, we are going to learn about MATLAB Interpolation. Interpolation methods can be used in creating various models in statistics. It is a procedure to estimate the points within a defined range. Interpolation is mainly used in mathematics, image scaling, and digital signal processing methods.
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