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Time series prediction model based on improved multi-objective differential optimization echo state network

A technology of echo state network and prediction model, which is applied in the direction of biological neural network model, computing model, biological model, etc., can solve problems such as difficult to determine, and achieve the effect of improving prediction accuracy, high robustness, and strong global search ability

Pending Publication Date: 2020-06-05
DALIAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to solve the problem that the reserve pool parameters of the echo state network are difficult to determine when dealing with different time series, and provide a prediction model that utilizes an improved multi-objective differential evolution algorithm to optimize the reserve pool parameters of the echo state network without artificial Manually adjust parameters to save time and apply to different time series forecasts to improve forecast accuracy

Method used

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  • Time series prediction model based on improved multi-objective differential optimization echo state network
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  • Time series prediction model based on improved multi-objective differential optimization echo state network

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Embodiment 1

[0060] Embodiment 1 The beneficial effect of the present invention is illustrated through the data sets of monthly average temperature and monthly rainfall in Dalian. The monthly average temperature and precipitation data set from January 1951 to December 2017 in Dalian was selected, with a total of 792 samples. The sampling interval is months. The monthly precipitation in Dalian is used as the second dimension of the experimental data to assist in the prediction of the monthly average temperature in Dalian. 75% of the data is used for training and 25% for testing. In the training samples, the states of the first 50 samples are discarded to clean up the influence of initial transients. In order to prove the effectiveness of the present invention, for PSO-ESN, AFSA-ESN, TLBO-ESN, IBEA-ESN, the present invention makes the range of ESN reserve pool parameters the same as shown in Table 1. The population size and maximum number of iterations are also the same. Table 2 shows th...

Embodiment 2

[0067] Preferably, embodiment two adopts Beijing PM from Beijing Air Pollution Index (AQI) 2.5 sequentially. The present invention uses Convergent Cross Mapping (CCM) for Beijing PM 2.5 Auxiliary variables for time series selection, found PM 10 , CO and SO 2 to PM 2.5 big impact. Therefore, the present invention selects PM 10 , CO and SO 2 as a PM 2.5 Auxiliary variables for time series forecasting. The present invention selects from January 2, 2016 to December 31, 2016, a total of 8759 samples are used for simulation. 70% of the total samples are used for training and 30% of the total samples are used for testing. In the training samples, the states of the first 100 samples are discarded to remove the influence of initial transients.

[0068] From Table 1, we can know the range settings of the reserve pool parameters. In order to prove the validity of the model proposed by the present invention, the present invention has carried out the same settings on the AFSA-ES...

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Abstract

The invention discloses a time series prediction model based on an improved multi-objective differential optimization echo state network. Firstly, a population is initialized randomly, and fitness evaluation is carried out on individuals in the population in sequence; secondly, the maximum number of iterations of the population is set for iteration; thirdly, differential evolution is adopted to generate variant individuals and test individuals for each individual in the population, the test individuals form a test population, and the contemporary population and the test population form a mixedpopulation; and the mixed population is decomposed into K sub-populations by utilizing a reference vector, and the sub-populations are updated. All updated sub-populations are new populations of thenext generation, and the previous step is returned. Finally, when the number of iterations reaches the maximum, a final population is obtained, and an individual is selected from the final populationas an optimal output individual. The improved multi-objective differential evolution algorithm is utilized to optimize the parameters of the storage pool, the optimization performance of the model isimproved, the proposed model can improve the prediction precision of the time sequence, and the model has good generalization ability and practical application value.

Description

technical field [0001] The invention relates to a complex time series prediction model, in particular to a time series prediction model based on an improved multi-objective differential optimization echo state network. Background technique [0002] Time series widely exist in all aspects of social life. It is widely used in economic fields, such as commodity prices, stock indexes. It is also widely used in industrial fields, such as bearing health monitoring. In the field of hydrology, annual runoff and galaxy morphology, among others. Therefore, the accuracy of time series forecasting is usually the goal pursued by many researchers. In the past few decades, various time series forecasting models have been proposed, such as autoregressive models, neural networks, support vector regression, and fuzzy systems. In particular, neural networks show great advantages in dealing with nonlinearities. With the continuous efforts of many scholars, the neural network has been deepl...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06N3/08
CPCG06N3/006G06N3/08
Inventor 任伟杰王依雯韩敏
Owner DALIAN UNIV OF TECH