DocumentCode
2176265
Title
A mimetic pattern generator for multiple periodic signals using recurrent neural networks
Author
Furutani, N. ; Fujimoto, K. ; Akutagawa, M. ; Kinouchi, Y. ; Nagashino, H.
Author_Institution
Dept. of Electr. & Electron. Eng., Tokushima Univ., Japan
Volume
2
fYear
2002
fDate
2-5 Dec. 2002
Firstpage
566
Abstract
A living body has a mechanism to acquire various periodic signals in the environment. In our nerve system, this is realized by using of simple neural oscillators. The oscillators optimize their internal parameters to synchronize with the external signal only from error between their own output and external signal. Furthermore, a single neural oscillator is considered to generate multiple complex signals. In this study, a simple neural oscillator model which can acquire external periodic signals is described. The proposed model is based on the multi-layered recurrent neural network which has time delayed feedback connections. A task to acquire two waveforms of Japanese vowels was applied to examine the capability of this model. According to results of computer simulations, it is capable to acquire these wave forms successfully.
Keywords
delays; feedback; recurrent neural nets; signal detection; signal generators; speech processing; Japanese vowels; computer simulation; internal parameters; mimetic pattern generator; multilayer recurrent neural networks; multiple complex signals; multiple periodic signals; optimisation; signal acquisition; signal sources; simple neural oscillators; single neural oscillator; speech wave forms; time delayed feedback; various periodic signals; Artificial neural networks; Biological system modeling; Computer simulation; Delay effects; Health and safety; Neural networks; Oscillators; Recurrent neural networks; Signal generators; Synchronization;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on
Print_ISBN
981-04-8364-3
Type
conf
DOI
10.1109/ICARCV.2002.1238486
Filename
1238486
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