Random Number Generation

In the MATLAB language, there exists a comprehensive collection of commonly used algorithms that includes numerous practical computational methods. Among these, random number generation stands out as an essential algorithm. This algorithm enables users to generate random numbers within specified ranges and allows parameter configuration for quantity and seed values. The implementation typically utilizes MATLAB's built-in functions like rand(), randi(), and randn(), which employ pseudorandom number generators based on sophisticated algorithms such as the Mersenne Twister. For reproducible results, the rng() function controls the random number seed. In practical applications, random number generation serves critical roles in simulation experiments, data encryption systems, Monte Carlo methods, and cryptographic protocols. Understanding and mastering this algorithm is therefore fundamental for both learning MATLAB programming and developing sophisticated technical applications.