Recurrent neural network design for temporal sequence learning
Hon Keung Kwan, J. Yan · 2002
Presents two designs of a recurrent neural network with 1st and 2nd order self-feedback at the hidden layer. The first design is based on a gradient descent algorithm and the second design is based on a genetic algorithm (GA). The simulation results of the single hidden layer network and those of a single hidden layer feedforward neural network for learning 50 commands of up to 3 words and 24 phone numbers of 10 digits are presented. Results indicate that the GA-based dynamic recurrent neural network is best in both convergence and error performance.