scholarly journals Combination Forecast of Economic Chaos Based on Improved Genetic Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yankun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Small Sample ◽  
Improved Genetic Algorithm ◽  
Tree Model ◽  
Combination Forecast ◽  
Bp Neural Network Model

The deterministic economic system will also produce chaotic dynamic behaviour, so economic chaos is getting more and more attention, and the research of economic chaos forecasting methods has become an important topic at present. The traditional economic chaos forecasting models are mostly based on large samples, but in actual production activities, there are a large number of small-sample economic chaos problems, and there is still no effective solution. This paper proposes a combined forecasting model based on the traditional economic chaos forecasting method. First of all, through the decision tree classification, priority selection of features, rough prediction is achieved. Secondly, we use BP neural network to make secondary prediction. Because the initial weight is randomly selected, it is easy to fall into the defect of local minimum. This paper optimizes the BP neural network. Finally, the decision tree model and the BP neural network model optimized by the improved genetic algorithm are combined, and the combined model is optimized by the improved GA. This method can take advantage of many prediction models and combine the prediction information of multiple different prediction models to effectively improve the fitting ability of the model and improve the prediction accuracy.

scholarly journals Air Combat Maneuver Trajectory Prediction Model of Target Based on Chaotic Theory and IGA-VNN

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Zhi-fei Xi ◽  
An Xu ◽  
Ying-xin Kou ◽  
Zhan-wu Li ◽  
Ai-wu Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Time Series ◽  
Bp Neural Network ◽  
Functional Model ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Trajectory Prediction ◽  
Target Trajectory ◽  
Chaotic Theory

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural network (IGA-VNN) model is proposed, mathematically deducing and analyzing the consistency between Volterra functional model and back propagation (BP) neural network in structure. Firstly, the C-C method is used to reconstruct the phase space of the target trajectory time series, and the maximum Lyapunov exponent of the time series of the target maneuver trajectory is calculated. It is proved that the time series of the target maneuver trajectory has chaotic characteristics, so the chaotic method can be used to predict the target trajectory time series. Then, the practicable Volterra functional model and BP neural network are combined together, learning the advantages of both and overcoming the difficulty in obtaining the high-order kernel function of the Volterra functional model. At the same time, an adaptive crossover mutation operator and a combination mutation operator based on the difference degree of gene segments are proposed to improve the traditional genetic algorithm; the improved genetic algorithm is used to optimize BP neural network, and the optimal initial weights and thresholds are obtained. Finally, the IGA-VNN model of chaotic time series is applied to the prediction of target maneuver trajectory time series, and the experimental results show that its estimated performance is obviously superior to other prediction algorithms.

scholarly journals Sales Forecasting Model Based on BP Neural Network Optimized by Improved Immune Genetic Algorithm

2021 ◽  
Author(s):  
Kunyu Cao ◽  
Yongdang Chen ◽  
Xinxin Song ◽  
Shan Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Forecasting Model ◽  
Antibody Concentration ◽  
Immune Genetic Algorithm ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract A new sales forecasting model based on an Improved Immune Genetic Algorithm (IIGA), IIGA that optimizes the BPNN (IIGA-BP) has been proposed. The IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operator and mutation operator, which effectively improved the convergence ability and optimization anility of IIGA. And IIGA can optimize the BPNN’s initial weights and threshold and improve the randomness of network parameters as well as the drawbacks that lead to output instability of BPNN and easiness into local minimum value. It taking the past records of sales figures of a certain steel enterprise as an example, utilizing the Matlab to construct the BP neural network, Immune Genetic Algorithm that optimizes the BPNN (IGA-BP), IGA-BP neural network, and IIGA-BP neural network prediction models for simulation and comparative analysis. The experiment demonstrates that IIGA-BP neural network prediction model possessing a higher prediction accuracy and more stable prediction effects.

scholarly journals Fault early warning of pitch system of wind turbine based on GA-BP neural network model

2020 ◽  
Vol 194 ◽  
pp. 03005
Author(s):  
Sihan Chen ◽  
Yongguang Ma ◽  
Liangyu Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Turbine ◽  
Early Warning ◽  
Bp Neural Network ◽  
Sliding Window ◽  
System Model ◽  
Scada System ◽  
Pitch System ◽  
Bp Neural Network Model

A fault early warning method based on genetic algorithm to optimize the BP neural network for the wind turbine pitch system is proposed. According to the parameters monitored by SCADA system, using correlation analysis to screen out the parameters of the pitch system with strong power correlation. The BP neural network optimized by genetic algorithm is used to establish the model of the pitch system under normal working conditions. The verification results show that the input parameters of the pitch system model determined by the correlation coefficient are more reasonable, and the accuracy of the pitch system model established by the genetic algorithm-optimized BP neural network is higher than that of the unoptimized model. Based on the above model, a sliding window model is established, and the early warning threshold is determined through the statistics of the residuals of the sliding window to realize the fault early warning of the pitch system of the wind turbine. The example shows that the method can give early warning in the event of failure, and verifies the effectiveness of the method.

Prediction of PM2.5 Mass Concentration Based on the Back Propagation (BP) Neural Network Optimized by t-Distribution Controlled Genetic Algorithm

2020 ◽  
Vol 15 (4) ◽  
pp. 432-441
Author(s):  
Peng S. Chen ◽  
Yong J. Zheng ◽  
Lin Li ◽  
Tao Jing ◽  
Xiao X. Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Mass Concentration ◽  
Monitoring And Control ◽  
Hourly Data ◽  
Bp Neural Network Model ◽  
T Distribution

In the past few years, human-health has been severely impacted from PM2.5 and has thus been a very popular topic of study. Furthermore, monitoring and control of PM2.5 are becoming one of the major environmental problems. In view of this, the present work targets at the establishment of an optimized BP neural network model based on t-distributed control genetic algorithm (BPM-TCG). Subsequently, in order to verify the performance of the proposed BPM-TCG, comparison analyses were performed among the prediction results generated from BPM-TCG, BP neural network model and BP-GA according to hourly data of PM2.5 mass concentration, analysis of corresponding meteorological factors, and gas pollutant concentrations from October 2017 to August 2018 at Qiqihar University monitoring point. The experimental results showed that BPM-TCG had the highest prediction accuracy and the best generalization ability, excellent applicability and commonality. Additionally, it may provide a basis for predicting the mass concentration of PM2.5, and thereby control and prevent the air pollution.

scholarly journals Genetic Algorithm – Back Propagation (GA-BP) Neural Network for Chlorophyll-a Concentration Inversion Using Landsat 8 OLI Data

2020 ◽  
Vol 143 ◽  
pp. 02002
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Ronghui Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Chl A ◽  
Bp Neural Network Model

Chlorophyll-a (Chl-a) accurate inversion in inland water is important for water environmental protection. In this study, we tested the Genetic Algorithm optimized Back Propagation (GA-BP) neural network model to precisely simulated the Chl-a in an inland lake using Landsat 8 OLI images. The result show that the R2 of GA-BP neural network model has increased 28.17% compared to traditional BP neural network model. Then this GA-BP model was applied to another two scenes of Landsat 8 OLI image with the R2 of 0.961, 0.954 respectively for March 26 2018, October 26 2018. And the spatial distribution have shown a reasonable result of Chl-a variation in Lake Donghu. This study can provide a new method for Chla concentration inversion in urban lakes and support water environment protection on a large scale.

Prediction Models of Short-Term Traffic Flow Based on Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2908-2911 ◽  
Author(s):  
Chao Jun Dong ◽  
Ang Cui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Topology ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Measured Data ◽  
Short Term ◽  
Hidden Layer ◽  
Bp Neural Network Model

For the city’s road conditions, a nonlinear regression prediction model based on BP Neural Network was built. The simulation shows it has good adaptability and strong nonlinear mapping ability. Using the wavelet basis function as hidden layer nodes transfer function, a BP-Neural- Network-topology-based Wavelet Neural Network model was proposed. The model can overcome the defects of the BP Neural Network model that easy to fall into local minimum and cannot perform global search. The feasibility of the model was proved using measured data from yingbin avenue in jiangmen city.

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