Non-Linear Regression Forecast Model and Learning Algorithm Based on Functional Networks
Fitting of forecast function is very difficult and important in non-linear regression forecast problems. The accuracy is directly affected by the fitting of forecast function. Linear model replaced non-linear model in the traditional method is difficult to solve the problem when non-linear is stronger, and the result of fitting and forecast is not ideal. Functional network is a recently introduced extension of neural networks. It has certain advantages solving non-linear problems. Non-linear regression forecast model and learning algorithm based on functional networks is proposed in this article. Example about multi-variable non-linear regression forecast is provided. The simulation results demonstrate that forecast model based on Functional Networks whose accuracy of fitting and forecasting is more than some traditional methods have some value about theory and application.
X. H. Liu, "Non-Linear Regression Forecast Model and Learning Algorithm Based on Functional Networks", Advanced Materials Research, Vol. 159, pp. 595-598, 2011