The Application for the Partial Least-Squares Regression (PLS) and Fuzzy Neural Networks Model (FNN) in the Wind Field Assessment

Abstract

Searching the predictors in each level of the NCEP data by use the long time series data of NCEP and short time sequence data of wind observation. And filtering the information and extraction the components for these primary predictors using the method of partial least-squares regression (PLS), then takes the new comprehensive variables (names components) as predictors and using the neural network with the features including adaptive and learning and the logical reasoning ability of fuzzy system to establish the wind field calculation model with fuzzy neural network(FNN) through combining fuzzy neural network system and adjustment the system parameters using BP algorithm. Comparing the calculation result shows that the errors of combining model with partial least-square regression(PLS) and fuzzy neural network(FNN) is smaller than that the multiple linear regression model. The length time sequence data of wind could be calculated according to the short time sequence data of observation wind and the long time series data of NCEP by the combining model with PLS and FNN in practical, therefore this model is better practicability and popularize value for it provide the basis to research the exploitation wind resources.

Low-cost sensors aided vehicular position prediction with partial least squares regression during GPS outage

“…Finally, if k is the number of PLS factors t 1 , t 2 , …, t k extracted in the basis of the accuracy requirements (Chen et al, 2011). Equation (4) which is for S 0 on t 1 , t 2 , …, t k can satisfy the least-square regression model.…”