Szkoła, J., Pancerz, K., Warchoł, J.: Improving Learning Ability of Recurrent Neural Networks: Experiments on Speech Signals of Patients with Laryngopathies. In: F. Babiloni, A. Fred, J. Filipe, H. Gamboa (Eds.), Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS'2011), Rome, Italy, January 26-29, 2011, pp. 360-364.

Recurrent neural networks can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks are a classical representative of this kind of neural networks. In the paper, we show how to improve learning ability of the Elman network by modifying and combining it with another kind of a recurrent neural network, namely, with the Jordan network. The modified Elman-Jordan network manifests a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients with laryngopathies.