Research on Partial Least-Squares Regression Model Based on Particle Swarm Optimization and Its Application

In order to improve the fitting and forecasting precision and solve the problem that some data have less sensitivity leading to low simulation precision of partial least-squares regression model, particle swarm optimization algorithm is adopted to optimize the partial regression coefficient, and then partial least-squares regression model based on particle swarm optimization is built. At the same time, the model is applied to forecast the frozen depth in Harbin area. Compared with the traditional partial least-squares regression model, the model after optimization has more reliability and stability. It also has higher fitting and forecasting precision.