Abstract
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.
Highlights
Determining the irregular movement in the system is called chaos. e chaotic time series is the manifestation of the discrete situation of chaos. e time series with chaotic characteristics generated by the chaotic model is the chaotic time series. e rich dynamics information is implied by the chaotic time series
We found that the mean square error (MSE), MAD, and mean absolute percentage error (MAPE) of the brain emotional network (BEN) model were all smaller than that of the MLP-BP neural network model, LM-BP neural network model, brain emotional learning (BEL) basic model, and SVM model. is shows that the prediction accuracy of the BEN model is very good, higher than that of common neural network models, but it is still insufficient compared with deep learning network
Based on the research of the existing brain emotional learning (BEL) model, this paper proposes the BEN model by changing the linear structure into a nonlinear structure and giving output weights to describe the influence of different structures. en we further propose the Ben-AGA model through the combination of adaptive genetic algorithm and BEN model. e weights to be optimized are encoded as chromosomes; the crossover process and the mutation process are controlled by the dynamic crossover probability and dynamic mutation probability of the design; and the chromosome is evaluated by the fitness function to select the best parameters
Summary
Determining the irregular movement in the system is called chaos. e chaotic time series is the manifestation of the discrete situation of chaos. e time series with chaotic characteristics generated by the chaotic model is the chaotic time series. e rich dynamics information is implied by the chaotic time series. Considering that the BEN network constructed in this paper is to study the prediction of chaotic time series, the genetic algorithm with stronger global search ability is adopted as the learning algorithm to optimize the parameters of the network model. The adaptive genetic algorithm is used to optimize the parameters of the BEN model, so that the calculation of the model is more accurate and efficient, and the problem of artificially setting reward signals that affect the prediction accuracy is avoided. BEN-AGA model prediction of chaotic time series needs to construct a BEN-AGA model first, input variables, and obtain all weight parameters in the BEN-AGA model through an adaptive genetic algorithm and update the weight parameters to the BEN-AGA model. Step 4: To input observed values into the Ben-AGA model, the fitness evaluation was carried out, and the optimal chromosome was selected by the adaptive genetic algorithm. Step 6: To obtain the predicted value of the chaotic time series by single-step prediction through the BEN-AGA model of the confirmed parameters
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