Abstract
This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out.
Highlights
With the continuous improvement of the living standard of the residents, people are pursuing more spiritual life enrichment under the condition of material base satisfaction [1]
The combination of fuzzy theory and neural network improves the situation, there is still the defect that it cannot handle the randomness and fuzziness of the data at the same time, and the process of determining the affiliation function needs to be determined artificially based on rich experience, which is influenced by subjective factors. e cloud model has great advantages in dealing with uncertainty and can realize the two-way conversion of qualitative data and qualitative concepts, but its method of finding numerical features is often determined according to the boundary of the data and cannot consider the whole data
We propose a method to find the numerical features of the cloud model, introduce the concept of cloud rules to determine the boundary, and improve the traditional “soft and” algorithm, to establish a new network model structure, cloud fuzzy neural network
Summary
With the continuous improvement of the living standard of the residents, people are pursuing more spiritual life enrichment under the condition of material base satisfaction [1]. Complexity of destination tourism network attention, make accurate predictions and corresponding reception measures for the real passenger flow, do a good job of peak season tourism warning, enrich off-season tourism products, implement refined management of ice and snow tourism destinations, and promote the sustainable development of ice and snow tourism destinations It can further verify the relationship between the information flow of network attention and the real tourism flow and provide a reference for passenger flow management [4]. From the perspective of system science, we construct a system dynamics model for ice and snow tourism market information research and use the modeling software Genism PLE to realize simulation results based on existing market information, verify the validity of the model, and provide reliable predictions and feasibility recommendations through the analysis of multiscenario simulation results of system dynamics, so that the government and tourism developers can better grasp investment opportunities and directions. Our improved fuzzy neural network algorithm is the first research on the competitiveness of ice and snow tourism, which is in-depth analysis of ice and snow tourism, and it has strong accuracy
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