Abstract
We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network.
| Original language | English |
|---|---|
| Pages (from-to) | 102-105 |
| Number of pages | 4 |
| Journal | Artificial Life and Robotics |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2006 Nov |
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Keywords
- Dynamic neural network
- Genetic algorithm
- Identification
- Property parameter
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Identification of time-series signals using a dynamic neural network with GA-based training. / Nakazono, Kunihiko; Ohnishi, Kouhei; Kinjo, Hiroshi.
In: Artificial Life and Robotics, Vol. 10, No. 2, 11.2006, p. 102-105.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Identification of time-series signals using a dynamic neural network with GA-based training
AU - Nakazono, Kunihiko
AU - Ohnishi, Kouhei
AU - Kinjo, Hiroshi
PY - 2006/11
Y1 - 2006/11
N2 - We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network.
AB - We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network.
KW - Dynamic neural network
KW - Genetic algorithm
KW - Identification
KW - Property parameter
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UR - http://www.scopus.com/inward/citedby.url?scp=33750698274&partnerID=8YFLogxK
U2 - 10.1007/s10015-005-0356-6
DO - 10.1007/s10015-005-0356-6
M3 - Article
VL - 10
SP - 102
EP - 105
JO - Artificial Life and Robotics
JF - Artificial Life and Robotics
SN - 1433-5298
IS - 2
ER -