An LMS algorithm for training single layer globally recursive neural networks

Unlike feedforward neural networks which can act as universal function approximators, recursive neural networks have the potential to act as both universal function approximators and universal system approximators. In this paper, a globally recursive neural network least mean square gradient descent or a real time recursive backpropagation algorithm is developed for a single layer globally recursive neural network that has multiple delays in its feedback path.