Authors:
Jarosław Szkoła
1
;
Krzysztof Pancerz
1
and
Jan Warchoł
2
Affiliations:
1
University of Information Technology and Management, Poland
;
2
Medical University of Lublin, Poland
Keyword(s):
Recurrent neural networks, Learning of neural networks, Laryngopathies, Temporal patterns.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
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.