TLS parameter estimation for filtering chaotic time series

Schweizer, S.M.; Stonick, V.L.; Evans, J.L.

doi:10.1109/icassp.1996.544111

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Due to the extreme sensitive to the initial conditions and parameters of the chaos, the initial conditions which approach to each other will separate exponentially, which cause errors propagation. In order to avoid error propagation and improve the estimation precision, some backward direction iteration technology, such as the dynamical programming implementation of ML estimation, the recursive ML algorithm and the halving method have been extensively studied [7][8][9][10][11][12]. On the other hand, the development of symbolic dynamics brings us some new light.…”

Section: Introductionmentioning

confidence: 99%

Recovery of Initial Conditions of Time-Varying CML with Symbolic Dynamics

Shen¹,

Zhang²,

Sun³

2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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A novel computationally efficient algorithm in terms of time-varying symbolic dynamics method is proposed to estimate the unknown initial conditions of coupled map lattices (CML). The presented method combines symbolic dynamics with time-varying control parameter to develop a time-varying scheme for estimating the initial condition of multi-dimensional spatial-temporal chaotic signals. The performance of the presented time-varying estimator is analyzed and compared with the common time-invariant estimator. Several simulations are carried out to show that the proposed method provides an efficient estimation of the initial condition of each lattice in the coupled system.

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Section: Introductionmentioning

confidence: 99%

Recovery of Initial Conditions of Time-Varying CML with Symbolic Dynamics

Shen¹,

Zhang²,

Sun³

2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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“…(5) The fusion method based on fuzzy, neural network. (6) The fusion method based on rough set theory, etc. In the above methods, the least squares, maximum likelihood estimation, Kalman filter method are all the effective theory basis of estimation theory.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are applied to the plant with accurate mathematical model, so they can not be applied to the complex control plant difficult to model. Bayesian estimation method is comparatively mature fusion method which can be applied to the controlled plant without model, hence Bayesian estimation method is applicable to ceramic kiln control system difficult to model [6] .…”

Section: Introductionmentioning

confidence: 99%

Intelligent Temperature Control of Ceramic Kiln Based on Data Fusion Technology

Zhu

Wan

2011

AMM

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At first, according to the feature of ceramic kiln, this paper studies a fundamental data fusion method which is applied to ceramic kiln temperature control system. This method is used to solve the parameter estimation problem in measurement noise environment. Then, it proposes a kind of intelligent control structure of ceramic kiln temperature control system based on multi-sensor data fusion technology. At last, this data fusion method is applied to intelligent temperature control system of ceramic kiln. The result shows that the method proposed is effective and feasible.

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“…In recent years, many researchers have addressed problems of nonlinear time series analysis such as the dynamical system modeling [1][2][3][4], the parameter estimation [5][6][7] and the noise reduction [S-11]. Most of the proposed methods deal with a fairly unitary task.…”

Section: Introductionmentioning

confidence: 99%

“…Now an accurate model and a clean signal are both cause and effect of each other. To solve this problem and get improved results of parameter estimation and noise reduction, Schweizer, et al [5] proposed a total least square (TLS) parameter estimation method. It can also do some noise reduction work at the same time, but the signal needs to be further processed with other method to improve the effect of noise reduction.…”

Section: Introductionmentioning

confidence: 99%

A cross-reference method of parameter estimation and noise reduction and its applications

Yang

1999

J. of Electron.(China)

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This paper proposes a cross-reference method of nonlinear time series analysis, combining the tasks of dynamical system parameter estimation and noise reduction which were fulfilled separately before. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior works can be viewed as special cases of this generM framework and effective new algorithms may be devised according to it. Two examples of chaotic time series analysis are also given to show the applicability of the proposed method.

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TLS parameter estimation for filtering chaotic time series

Cited by 5 publications

References 7 publications

Recovery of Initial Conditions of Time-Varying CML with Symbolic Dynamics

Recovery of Initial Conditions of Time-Varying CML with Symbolic Dynamics

Intelligent Temperature Control of Ceramic Kiln Based on Data Fusion Technology

A cross-reference method of parameter estimation and noise reduction and its applications

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