Narrow Results

In order to further explore the important impact of inbound tourism on economic development, six cities such as X city are selected as the research objects. Based on the panel threshold regression model and relevant data, the dynamic nonlinear relationship between inbound tourism and the economic development of the tertiary industry is explored through mathematical empirical analysis. The specific research results are as follows. The inbound tourism of X city develops rapidly and is in the stage of accelerating development. Meanwhile, the development of inbound tourism in X city has a certain pulling effect on the development of related industries and the tertiary industry. Among them, the elasticity coefficients of inbound tourism in X city to transportation industry, accommodation and catering industry, wholesale and retail industry, finance and insurance industry, and real estate industry are 1.137, 1.104, 1.134, 1.105, and 1.062, respectively; the elasticity coefficient to the income of the tertiary industry is 0.436. Moreover, the analysis of the relationship between inbound tourism and the development of the tertiary industry in six cities shows that there is a dynamic nonlinear relationship between them, and the research results are in line with the expected hypothesis. The research results can provide scientific and effective reference for the follow-up study of inbound tourism and economics.

With the development of industrial technology, acceleration sensors have a wide range of applications. The application environment has strict requirements on acceleration sensor, which needs it to get accurate input and output response in complexity. According to the Hammerstein model, this paper studies the dynamic nonlinear relationship between the output voltage and the input acceleration of the acceleration sensor that can be divided into static nonlinear component and dynamic linear component. We combine the least square method with adaptive neural network to calculate the parameters of static nonlinear and dynamic linear components. The least square method is used to improve the training performance of the network and avoid the network falling into the local minimum of the traditional neural network. Experimental results show that compared with other methods, the proposed method has the advantages of less training steps and strong approximation ability, and the algorithm is less affected by external noise. This method can realize nonlinear system identification of acceleration sensor and provide reliable basis for compensation.