Traffic Flow Prediction Model of BP Neural Network Based on Adaptive Genetic Algorithm Optimization

2020 ◽  
Vol 09 (08) ◽  
pp. 1317-1326
Author(s):  
明州 汪
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Junxi Zhang ◽  
Shiru Qu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Population Diversity ◽  
Evolution Process ◽  
Adaptive Genetic Algorithm ◽  
Generalization Performance ◽  
Traffic Flow Prediction ◽  
Output Error ◽  
Flow Prediction

This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm (GA) are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory (MATLAB) platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution.

Hangzhou Housing Demand Forecasting Model Based on BP Neural Network of Genetic Algorithm Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 37-41 ◽  
Author(s):  
Yi Hua Mao ◽  
Meng Bo Zhang ◽  
Ning Bo Yao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Housing Market ◽  
Bp Neural Network ◽  
Housing Demand ◽  
Forecasting Model ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
The Impact

Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

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