MATLAB Implementation of Artificial Neural Networks for Tourism Forecasting

MATLAB programming for artificial neural networks, particularly the BP network implementation for predicting inbound tourist numbers, holds significant reference value for tourism management departments and government agencies. The development of a scientifically reasonable prediction model using a three-layer BP neural network architecture - consisting of an input layer, hidden layer, and output layer - ensures the accuracy and reliability of forecasting outcomes. The implementation typically involves defining network parameters such as learning rate, training epochs, and activation functions (commonly using tansig or logsig functions). Key MATLAB functions like newff for network creation, train for training, and sim for simulation are essential components. Beyond predicting inbound tourist numbers, this model can be extended to analyze tourism industry development trends and track changes in tourism demand. These analytical insights provide crucial reference points for tourism management and government departments when formulating relevant policies and making informed decisions. The backpropagation algorithm employed automatically adjusts connection weights through gradient descent optimization, minimizing prediction errors during the training process.