Local Adaptive Nonlinear Filter Prediction Model with a Parameter for Chaotic Time Series

2010 ◽  
Vol 44-47 ◽  
pp. 3180-3184
Author(s):  
Fen Fang ◽  
Hai Yan Wang ◽  
Zhou Mu Yang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Gradient Descent ◽  
Chaotic Systems ◽  
Lorenz System ◽  
Logistic Map ◽  
Predictive Performance ◽  
Nonlinear Filter ◽  
Chaotic Time Series ◽  
Proposed Model

In order to improve the predictive performance for chaotic time series， we propose a novel local adaptive nonlinear filter prediction model. We use a function with a parameter to build an adaptive nonlinear filter in this model, and we train this model with an adaptive algorithm, deduced by the minimum square-root-error criterion and the steepest gradient descent rule. We evaluate the proposed model using four well-known chaotic systems, namely Logistic map, Henon map, Lorenz system and Rosslor system. All the results show a remarkable increase in predictive performance, comparing with the local adaptive nonlinear filter prediction model.

scholarly journals Constructing Digitized Chaotic Time Series with a Guaranteed Enhanced Period

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chuanfu Wang ◽  
Qun Ding
Keyword(s):  
Time Series ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
Logistic Map ◽  
Approximate Entropy ◽  
Chaotic Time Series ◽  
Pseudorandom Sequence ◽  
Periodic Behavior ◽  
Experimental Implementation ◽  
Sequence Generator

When chaotic systems are realized in digital circuits, their chaotic behavior will degenerate into short periodic behavior. Short periodic behavior brings hidden dangers to the application of digitized chaotic systems. In this paper, an approach based on the introduction of additional parameters to counteract the short periodic behavior of digitized chaotic time series is discussed. We analyze the ways that perturbation sources are introduced in parameters and variables and prove that the period of digitized chaotic time series generated by a digitized logistic map is improved efficiently. Furthermore, experimental implementation shows that the digitized chaotic time series has great complexity, approximate entropy, and randomness, and the perturbed digitized logistic map can be used as a secure pseudorandom sequence generator for information encryption.

scholarly journals A Resource-Allocating Network for Function Interpolation

1991 ◽  
Vol 3 (2) ◽  
pp. 213-225 ◽  
Author(s):  
John Platt
Keyword(s):  
Time Series ◽  
Learning Process ◽  
Gradient Descent ◽  
Chaotic Time Series ◽  
Local Region ◽  
Unusual Pattern ◽  
Network Parameters ◽  
Learning Patterns ◽  
Compact Representations

We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.

State-space prediction model for chaotic time series

1998 ◽  
Vol 58 (2) ◽  
pp. 2640-2643 ◽  
Author(s):  
A. K. Alparslan ◽  
M. Sayar ◽  
A. R. Atilgan
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
State Space ◽  
Chaotic Time Series

An Improved Local Weighted Linear Prediction Model for Chaotic Time Series

2014 ◽  
Vol 31 (2) ◽  
pp. 020503 ◽  
Author(s):  
Jian-Ling Qu ◽  
Xiao-Fei Wang ◽  
Yu-Chuan Qiao ◽  
Feng Gao ◽  
Ya-Zhou Di
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Linear Prediction ◽  
Chaotic Time Series ◽  
Linear Prediction Model

scholarly journals Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Congqing Wang ◽  
Linfeng Wu
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Prediction Model ◽  
Dynamic Model ◽  
Chaotic Time Series ◽  
Joint Torque ◽  
Support Vector ◽  
Space Manipulator ◽  
Chaotic Vibration ◽  
Vibration Prediction

The dynamic model of a planar free-floating flexible redundant space manipulator with three joints is derived by the assumed modes method, Lagrange principle, and momentum conservation. According to minimal joint torque’s optimization (MJTO), the state equations of the dynamic model for the free-floating redundant space manipulator are described. The PD control using the tracking position error and velocity error in the manipulator is introduced. Then, the chaotic dynamic behavior of the manipulator is analyzed by chaotic numerical methods, in which time series, phase plane portrait, Poincaré map, and Lyapunov exponents are used to analyze the chaotic behavior of the manipulator. Under certain conditions for the joint torque optimization and initial values, chaotic vibration motion of the space manipulator can be observed. The chaotic time series prediction scheme for the space manipulator is presented based on the theory of phase space reconstruction under Takens’ embedding theorem. The trajectories of phase space can be reconstructed in embedding space, which are equivalent to the original space manipulator in dynamics. The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function. It has been proved that the SVR prediction model has a good performance of prediction. The experimental results show the effectiveness of the presented method.

scholarly journals Heart disease prediction model with k-nearest neighbor algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 225
Author(s):  
Tssehay Admassu Assegie
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
Predictive Performance ◽  
Data Repository ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
K Nearest Neighbor Algorithm ◽  
Learning Data

<span>In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%</span>

scholarly journals An Accurate Online Prediction Model for Kiln Head Temperature Chaotic Time Series

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44288-44299
Author(s):  
Mingyang Lv ◽  
Xiaogang Zhang ◽  
Hua Chen ◽  
Chuanwu Ling ◽  
Jianmin Li
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Chaotic Time Series ◽  
Online Prediction ◽  
Head Temperature

Nonlinear Complexity and Chaotic Behaviors on Finite-Range Stochastic Epidemic Financial Dynamics

2019 ◽  
Vol 29 (06) ◽  
pp. 1950083
Author(s):  
Guochao Wang ◽  
Shenzhou Zheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Financial Time Series ◽  
Logistic Map ◽  
Entropy Method ◽  
Finite Range ◽  
Price Model ◽  
Nonlinear Complexity ◽  
Proposed Model ◽  
Real Market

In this paper, a novel stochastic financial price model, based on the theory of finite-range stochastic interacting epidemic system, is proposed to reproduce the nonlinear dynamic mechanism of price fluctuations in financial markets. To better understand the complexity behavior of the proposed model, we develop a new entropy-based approach called index fluctuation fuzzy entropy (IFFE) and construct four measure criteria. The effectiveness of this approach is experimentally validated by logistic map time series, white noise time series, [Formula: see text] noise time series and six financial time series. Moreover, the largest Lyapunov exponents and Kolmogorov–Sinai entropy method are applied to analyze the chaotic property of the proposed model. To verify the rationality of the proposed model, the same analyses for the real market data are comparatively investigated with the simulation ones. The empirical results reveal that the novel financial price model is able to reproduce some important features of the financial markets.

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