Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network

A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.

Research on risk evaluation of internet strategic transformation of manufacturing enterprises based on bp artificial neural network

For manufacturing enterprises to successfully enter the “Industry 4.0” era and establish advantages in the new wave of industrial revolution, they must use Internet thinking to transform manufacturing enterprises and promote the in-depth integration of informatization and industrialization under the premise of managing and controlling risks, to achieve transformation and upgrading. The innovation and management of the risks of manufacturing enterprises’ Internet strategic transformation directly affects the success or failure of enterprises’ transformation. On the basis of constructing the risk evaluation index system of manufacturing enterprise’s Internet strategy transformation, a manufacturing enterprise’s Internet strategy transformation risk evaluation model was constructed based on BP artificial neural network, and typical enterprises were selected to carry out practical application. The results show that the BP artificial neural network structure of the risk evaluation of the Internet strategic transformation of the manufacturing enterprises includes 18 layers of network input layer, 4 nodes of network hidden layer and 4 neurons of output layer, and the application effect of model is good.