• Title, Summary, Keyword: intelligent time series

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Fuzzy time-series model of fuzzy number observations (퍼지 넘버 연산에 의한 퍼지 시계열 모형)

  • Hong, Dug-Hun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.139-144
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    • 2000
  • Recently, a homogeneous fuzzy time series model was proposed by means of defining some new operations on fuzzy numbers. In this paper, we consider expanding the results to the nonhomogeneous fuzzy time series and the general fuzzy time series using Tw, the weakest t-norm, based algebraic fuzzy operations.

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Pattern recognition of time series data based on the chaotic feature extracrtion (카오스 특징 추출에 의한 시계열 신호의 패턴인식)

  • 이호섭;공성곤
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.294-297
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    • 1996
  • This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

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Fuzzy Logic-based Modeling of a Score (퍼지 이론을 이용한 악보의 모델링)

  • 손세호;권순학
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.211-214
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    • 2001
  • In this paper, we interpret a score as a time series and deal with the fuzzy logic-based modeling of it. The musical notes in a score represent a lot of information about the length of a sound and pitches, etc. In this paper, using melodies, tones and pitches in a score, we transform data on a score into a time series. Once more, we form the new time series by sliding a window through the time series. For analyzing the time series data, we make use of the Box-Jenkinss time series analysis. On the basis of the identified characteristics of time series, we construct the fuzz model.

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Chaotic Forecast of Time-Series Data Using Inverse Wavelet Transform

  • Matsumoto, Yoshiyuki;Yabuuchi, Yoshiyuki;Watada, Junzo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.338-341
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    • 2003
  • Recently, the chaotic method is employed to forecast a near future of uncertain phenomena. This method makes it possible by restructuring an attractor of given time-series data in multi-dimensional space through Takens' embedding theory. However, many economical time-series data are not sufficiently chaotic. In other words, it is hard to forecast the future trend of such economical data on the basis of chaotic theory. In this paper, time-series data are divided into wave components using wavelet transform. It is shown that some divided components of time-series data show much more chaotic in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided component enables us to precisely forecast the value or the movement of the time-series data in near future. The up and down movement of TOPICS value is shown so highly predicted by this method as 70%.

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Dimension Analysis of Chaotic Time Series Using Self Generating Neuro Fuzzy Model

  • Katayama, Ryu;Kuwata, Kaihei;Kajitani, Yuji;Watanabe, Masahide;Nishida, Yukiteru
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.857-860
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    • 1993
  • In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

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Electricity Price Prediction Model Based on Simultaneous Perturbation Stochastic Approximation

  • Ko, Hee-Sang;Lee, Kwang-Y.;Kim, Ho-Chan
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.14-19
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    • 2008
  • The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

Fuzzy Logic-based Modeling of a Score (퍼지 이론을 이용한 악보의 모델링)

  • 손세호;권순학
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.3
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    • pp.264-269
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    • 2001
  • In this paper, we interpret a score as a time series and deal with the fuzzy logic-based modeling of it. The musical notes in a score represent a lot of information about the length of a sound and pitches, etc. In this paper, using melodies, tones and pitches in a score, we transform data on a score into a time series. Once more, we foml the new Lime series by sliding a window through the time series. For analyzing the time series data, we make use of the Box-Jenkins s time series analysis. On the basis of the identified characteristics of time series, we construct the fuzzy model.

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Intelligent Digital Redesign of Uncertain Nonlinear Systems Using Power Series (Power Series를 이용한 불확실성을 포함된 비선형 시스템의 지능형 디지털 재설계)

  • Sung, Hwa-Chang;Joo, Young-Hoon;Park, Jin-Bae;Kim, Do-Wan
    • Proceedings of the KIEE Conference
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    • pp.496-498
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    • 2005
  • This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).

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Nonlinear Behavior in Love Model with Discontinuous External Force

  • Bae, Youngchul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.1
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    • pp.64-71
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    • 2016
  • This paper proposes nonlinear behavior in a love model for Romeo and Juliet with an external force of discontinuous time. We investigated the periodic motion and chaotic behavior in the love model by using time series and phase portraits with respect to some variable and fixed parameters. The computer simulation results confirmed that the proposed love model with an external force of discontinuous time shows periodic motion and chaotic behavior with respect to parameter variation.

Nonlinear Time Series Analysis Tool and its Application to EEG

  • Kim, Eung-Soo;Park, Kyung-Gyu
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.104-112
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    • 2001
  • Simply, Nonlinear dynamics theory means the complicated and noise-like phenomena originated form nonlinearity involved in deterministic dynamical system. An almost all the natural signals have nonlinear property. However, there exist few analysis software tool or package for a research and development of applications. We develop nonlinear time series analysis simulator is to provide a common and useful tool for this purpose and to promote research and development of nonlinear dynamics theory. This simulator is consists of the following four modules such as generation module, preprocessing module, analysis module and ICA module. In this paper, we applied to Electroencephalograph (EEG), as it turned out, our simulator is able to analyze nonlinear time series. Besides, we could get the useful results using the various parameters. These results are used to diagnostic the brain diseases.

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