Chaotic dynamics of time-delay neural networks

Jose C. Principe; Pei-Chen Lo

Chaotic dynamics of time-delay neural networks

Jose C. Principe^*, Pei-Chen Lo

^*Corresponding author for this work

電控工程研究所

研究成果: Paper › 同行評審

1 引文斯高帕斯（Scopus）

摘要

A study is made of the nonlinear dynamical properties of a class of recurrent artificial neural networks with time delay that have been proposed in the literature to learn and recognize time-varying input patterns. Their nonlinear dynamics are studied to see if they have the potential to model nonlinear dynamical behavior of biological field potentials such as the electroencephalogram (EEG). It is shown that changing the value of the weights in a periodic fashion causes the output of time-delay neural networks to show a correlation dimension in the EEG range.

原文	English
頁面	403-409
頁數	7
出版狀態	Published - 1 十二月 1990
事件	1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3) - San Diego, CA, USA 持續時間: 17 六月 1990 → 21 六月 1990

Conference

Conference	1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3)
城市	San Diego, CA, USA
期間	17/06/90 → 21/06/90

指紋深入研究「Chaotic dynamics of time-delay neural networks」主題。共同形成了獨特的指紋。

引用此

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title = "Chaotic dynamics of time-delay neural networks",

abstract = "A study is made of the nonlinear dynamical properties of a class of recurrent artificial neural networks with time delay that have been proposed in the literature to learn and recognize time-varying input patterns. Their nonlinear dynamics are studied to see if they have the potential to model nonlinear dynamical behavior of biological field potentials such as the electroencephalogram (EEG). It is shown that changing the value of the weights in a periodic fashion causes the output of time-delay neural networks to show a correlation dimension in the EEG range.",

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AB - A study is made of the nonlinear dynamical properties of a class of recurrent artificial neural networks with time delay that have been proposed in the literature to learn and recognize time-varying input patterns. Their nonlinear dynamics are studied to see if they have the potential to model nonlinear dynamical behavior of biological field potentials such as the electroencephalogram (EEG). It is shown that changing the value of the weights in a periodic fashion causes the output of time-delay neural networks to show a correlation dimension in the EEG range.

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