Forecast model of perceived demand of museum tourists based on neural network integration
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S.I. : DPTA CONFERENCE 2019
Forecast model of perceived demand of museum tourists based on neural network integration Yuan Gao1 Received: 20 February 2020 / Accepted: 2 May 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract With the development of experiential tourism and the improvement of people’s living standards, people have begun to transform tourist destinations into museum tourism. However, no effective method for predicting the demand for museum tourism has yet emerged. In order to be able to build a prediction model that can perceive the needs of museum tourists, this article uses advanced algorithms based on neural network integration and calls different algorithms: QPSO-BPNN, QPSO, PSO, PSO-BPNN, and BPNN. When the training ratio increases to 90%, the prediction accuracy of the three algorithms, BPNN, PSO, and PSO-BPNN, is less than 80%, and the prediction accuracy of the QPSO-BPNN algorithm has reached 92.5%. Under the condition of equal training set ratio, the prediction accuracy of QPSO-BPNN algorithm is always significantly higher than that of PSO-BPNN algorithm. When the training set proportions are 50%, 70%, and 90%, the changes in population size parameters have little effect on the prediction accuracy of the algorithm. Based on the above experiments, it is known that the QPSO-BPNN algorithm is less sensitive to the size of the population, and the algorithm has good robustness. With the increase in the number of initial classifiers, the prediction accuracy of the QPSO-BPNN algorithm has improved significantly. The experimental results are consistent with the previous theoretical derivation analysis, and the accuracy of the algorithm has a positive correlation with the number of classifiers. Keywords Tourist perception Neural network integration BP neural network (BPNN) Particle swarm optimization (PSO) Quantum particle swarm optimization (QPSO)
1 Introduction With the development of experience tourism and the transformation of museum functions to diversification, people are no longer satisfied with the popular science knowledge displayed by museums. The focus of the visit and display requirements provided by museums is gradually turning to experience perception, expecting museums to provide visual, auditory, emotional and other aspects of enjoyment during the visit, and experience higher-level cultural experience. How does the experience perception of tourists in the process of Museum Tour affect the tourists’ willingness to revisit? The difference of the influence of different experiences on tourists’ willingness to revisit is worthy of in-depth discussion. It will be of guiding and & Yuan Gao [email protected] 1
School of Economics and Management, Northwest University, Xi’an 710127, Shaanxi, China
practical significance to the image building, architectural design, display and layout, supporting project development, tourist development, publicity, and marketing of the museum. For research on tourism prediction and
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