The Forecasting Research of Beijing Tourism Demand Based on the BP Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 128-131 ◽  
Author(s):  
Yang Yu ◽  
Shi Min Wang
Keyword(s):  
Neural Network ◽  
Empirical Research ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Tourism Demand ◽  
Basic Principles ◽  
The Future

This paper describes the basic principles and algorithm of the BP neural network and builds a forecasting model of Beijing tourism demand based on the BP neural network. The forecasting model can forecast and analyze the number of tourists in Beijing in the future, which using the MATLAB tools and the number of tourists in Beijing during 1994 to 2012 for empirical research. The results show that the forecasting model of Beijing tourism demand based on the BP neural network can forecast the number of tourists in Beijing in the future more accurately.

scholarly journals Tourism Demand Forecasting Based on Grey Model and BP Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xing Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grey Model ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Tourism Demand ◽  
Combination Model ◽  
Combination Forecasting ◽  
Domestic Tourism

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

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.

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.

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