![]() The current state s of the uniform number generator can be obtained by typing s = rand (‘state’). To generate the same sequence, you must use the same state each time. In order to compare the results of two or more simulations, you sometimes will need to generate the same sequence of random numbers each time the simulation runs. Typing rand ( size (A) ) produces an array of random entries that is the same size as A.įor example, the following script makes a random choice between two equally probable alternatives. Use Y = rand (m, n, p, … ) to generate a multidimensional array Y having random elements. Even though there is a single call to the rand function, the rand function’s calculation has the effect of using a different state to obtain each of the 100 numbers so that they will be random. For example, to create a 1 x 100 vector y having 100 random values in the interval, type y = rand (1 ,100 ).Using the rand function this way is equivalent to typing rand 100 times. Type rand (m, n) to obtain an m x n matrix of random numbers. Type rand (n) to obtain an n x n matrix of uniformly distributed random numbers in the interval. Thus every time the rand function is used, a different result will be obtained. MATLAB obtains this state from the computer’s CPU clock. Typing rand again generates a different number because the MATLAB algorithm used for the rand function requires a “state” to start. Type rand to obtain a single random number in the interval. The MATLAB function rand generates random numbers uniformly distributed over the interval. In a sequence of uniformly distributed random numbers, all values within a given interval are equally likely to occur. The simulation program is executed many times, using a random set of numbers to represent the failure of one or more components, and the results are used to estimate the desired probability. In such cases engineers often resort to simulation to make predictions. age’of the components, but we often cannot obtain a function to describe . consisting of many components will fail is a function of the number and the. For example, the probability that a circuit. We often do not have a simple probability distribution to describe the distribution of outcomes in many engineering applications.
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