scholarly journals An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Yuanxin Wang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Decomposition Technique ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Application Analysis ◽  
Evolution Algorithm ◽  
Decomposition Number ◽  
Selection Of

Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

scholarly journals Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhenpeng Tang ◽  
Tingting Zhang ◽  
Junchuan Wu ◽  
Xiaoxu Du ◽  
Kaijie Chen
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hybrid Model ◽  
Stock Price ◽  
Differential Evolution Algorithm ◽  
Prediction Performance ◽  
Stock Index ◽  
Decomposition Technique ◽  
Mode Decomposition ◽  
Learning Machine

The prediction research of the stock market prices is of great significance. Based on the secondary decomposition techniques of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), this paper constructs a new hybrid prediction model by combining with extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm. The hybrid model applies VMD technology to the original stock index price sequence to obtain different modal components and the residual item, then applies EEMD technology to the residual item, and then superimposes the prediction results of the DE-ELM model for each modal component and the residual item to obtain the final prediction results. In order to verify the validity of the model, this paper constructs a series of benchmark models and, respectively, tests the samples of the S&P 500 index and the HS300 index by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model proposed in this paper achieves the best prediction performance in all prediction scenarios, which indicates that the modeling idea focusing on the residual term effectively improves the prediction performance of the model. In addition, the prediction effect of the model combined with the decomposition technology is superior to the single DE-ELM model, where the secondary decomposition technique has a significant decomposition advantage compared to the single decomposition technique.

A New Differential Evolution Algorithm with Random Parameters

2014 ◽  
Vol 556-562 ◽  
pp. 3614-3621
Author(s):  
Shao Rong Huang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Experimental Results ◽  
Random Numbers ◽  
Optimal Selection ◽  
Random Parameters ◽  
Evolution Algorithm ◽  
Selection Of

Differential evolution algorithm’s performance often depends heavily on the parameter settings. Based on analyzing the influence of the parameters setting in the experiment, the effects and the optimal selection of those major parameters on DE are analyzed, and some conclusions are derived. A new differential evolution algorithm which the scale constant (F) and crossover constant (CR) are generated as random numbers within a certain range in each iteration process is proposed. The experimental results shows that the new algorithm is simple, easy to realize and can get higher precision and better stability.

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