Obtaining functional form for chaotic time series evolution using genetic algorithm

1999 ◽  
Vol 9 (3) ◽  
pp. 789-794 ◽  
Author(s):  
Vamsi K. Yadavalli ◽  
Rahul K. Dahule ◽  
Sanjeev S. Tambe ◽  
B. D. Kulkarni
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Functional Form ◽  
Chaotic Time Series

RBF Network Based on Improved Niche Hybrid Hierarchy Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 775-780
Author(s):  
Fang Wang ◽  
Jin Lan Yu ◽  
Pin Chang Zhu ◽  
Xi Feng Wei
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Chaotic Time Series ◽  
Premature Convergence ◽  
Rbf Network ◽  
Operation Speed ◽  
System Resource ◽  
Simulation Results

The improved niche hybrid hierarchy genetic algorithm is presented to overcome the premature convergence which happens in genetic algorithm constructing RBF network. The niche with poor fitness of every individual is eliminated to save system resource and raise operation speed. The simulation results demonstrate the better predicted performance on the Mackey-Glass chaotic time series than other algorithms.

scholarly journals Prediction of Chaotic Time Series Based on BEN-AGA Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
LiYun Su ◽  
Fan Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Network Model ◽  
Fitness Function ◽  
Activation Function ◽  
Single Step ◽  
Chaotic Time Series ◽  
Adaptive Genetic Algorithm ◽  
Observation Sequence ◽  
The Brain

Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series. First, we improve the emotional brain learning (BEL) model using the activation function to change the two linear structures the amygdala and the orbitofrontal cortex into the nonlinear structure, and then we establish the brain emotional network (BEN) model. The brain emotional network model has stronger nonlinear calculation ability and generalization ability. Next, we use the adaptive genetic algorithm to optimize the parameters of the brain emotional network model. The weights to be optimized in the model are coded as chromosomes. We design the dynamic crossover probability and mutation probability to control the crossover process and the mutation process, and the optimal parameters are selected through the fitness function to evaluate the chromosome. In this way, we increase the approximation capability of the model and increase the calculation speed of the model. Finally, we reconstruct the phase space of the observation sequence based on the short-term predictability of the chaotic time series; then we establish a brain emotional network model and optimize its parameters with an adaptive genetic algorithm and perform a single-step prediction on the optimized model to obtain the prediction error. The model proposed in this paper is applied to the prediction of Rossler chaotic time series and sunspot chaotic time series. The experimental results verify the effectiveness of the BEN-AGA model and show that this model has higher prediction accuracy and more stability than other methods.

scholarly journals Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiuge Tan
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Time Series ◽  
Phase Space ◽  
Chaotic Time Series ◽  
Predictive Analysis ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Decision Algorithm ◽  
Study Results

The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity can perform nonrepetitive searches in a global scope, hence improving the algorithm’s accuracy and efficiency. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the prediction of economic chaotic time series, elaborated the development background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted phase space reconstruction and correlation dimension calculation, and finally carried out a simulation experiment with its result analysis. The study results show that the algorithm of chaotic genetics combined with fuzzy decision-making can dynamically adjust chaotic mutation operators and summarize fussy expert experiences. The phase space of its reconstructed chaotic attractor has high-precision predictability and can find orderly processes from changeable economic results, which in turn can be used to analyze and predict the complex economic chaotic time series. The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm.

scholarly journals Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Wang ◽  
Bi-hua Zhou ◽  
Shu-dao Zhou ◽  
Zheng Sheng
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Chaotic Time Series ◽  
Genetic Operators ◽  
Genetic Simulated Annealing Algorithm ◽  
Function Expression ◽  
Annealing Operation

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

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