Generation of pseudorandom numbers with arbitrary distribution by learnable look-up-table-type neural networks

The aim of the present manuscript is to propose a pseudo-random number generation algorithm based on a learnable non-linear neural network whose implementation is based on look-up tables. The proposed neural network is able to generate pseudo-random numbers with arbitrary distribution on the basis of standard variate generators available within programming environments. The proposed method is not computationally demanding and easy to implement on a computer. Numerical tests confirm the agreement between the desired and obtained distributions of the generated pseudo-random number batches. © 2008 IEEE.