• Title/Summary/Keyword: 리아프노프 지수

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Integrated Circuit Implementation and Characteristic Analysis of a CMOS Chaotic Neuron for Chaotic Neural Networks (카오스 신경망을 위한 CMOS 혼돈 뉴런의 집적회로 구현 및 특성 해석)

  • Song, Han-Jeong;Gwak, Gye-Dal
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.5
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    • pp.45-53
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    • 2000
  • This paper presents an analysis of the dynamical behavor in the chaotic neuron fabricated using 0.8${\mu}{\textrm}{m}$ single poly CMOS technology. An approximated empirical equation models for the sigmoid output function and chaos generative block of the chaotic neuron are extracted from the measurement data. Then the dynamical responses of the chaotic neuron such as biurcation diagram, frequency responses, Lyapunov exponent, and average firing rate are calculated with numerical analysis. In addition, we construct the chaotic neural networks which are composed of two chaotic neurons with four synapses and obtain bifurcation diagram according to synaptic weight variation. And results of experiments in the single chaotic neuron and chaotic neural networks by two neurons with the $\pm$2.5V power supply and sampling clock frequency of 10KHz are shown and compared with the simulated results.

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Stress status classification based on EEG signals (뇌파 신호 기반 스트레스 상태 분류)

  • Kang, Jun-Su;Jang, Giljin;Lee, Minho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.16 no.3
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    • pp.103-108
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    • 2016
  • In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, ${\frac{{\theta}\;power}{mid\;{\beta}\;power}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.

Analysis of Intrinsic Patterns of Time Series Based on Chaos Theory: Focusing on Roulette and KOSPI200 Index Future (카오스 이론 기반 시계열의 내재적 패턴분석: 룰렛과 KOSPI200 지수선물 데이터 대상)

  • Lee, HeeChul;Kim, HongGon;Kim, Hee-Woong
    • Knowledge Management Research
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    • v.22 no.4
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    • pp.119-133
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    • 2021
  • As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.