Nonnegative Least Squares Learning for the Random Neural Network

In this paper, a novel supervised batch learning algorithm for the Random Neural Network (RNN) is proposed. The RNN equations associated with training are purposively approximated to obtain a linear Nonnegative Least Squares (NNLS) problem that is strictly convex and can be solved to optimality. Following a review of selected algorithms, a simple and efficient approach is employed after being identified to be able to deal with large scale NNLS problems. The proposed algorithm is applied to a combinatorial optimzation problem emerging in disaster management, and is shown to have better performance than the standard gradient descent algorithm for the RNN.