基于泛函网络的非线性回归预测模型及学习算法

在非线性回归预测中，预测函数的拟合是其难点和关键，直接影响预测精度。当系统非线性较强时，传统方法不易于处理，拟合和预测结果不理想。泛函网络是最近提出的一种对神经网络的有效推广，在处理非线性问题时有一定的优势。为此提出了基于泛函网络的非线性回归预测模型和相应的学习算法。并分别就一元非线性回归预测和多元非线性回归预测给出了相应的实例。计算机仿真结果表明，泛函网络预测模型拟合度和预测精度都明显高于某些传统的方法，有较好的理论和应用价值。

Non-linear regression forecast model based on functional networks and learning algorithm

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.Non-linear model in the traditional method is difficult to solve the system whose non-linear is stronger,and the result of fitting and forecast is not ideal.Function 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 are proposed in this article.Some examples about one-dimensional non-linear regression forecast and multi-dimensional non-linear regression forecast are provided.The simulation results demonstrate that forecast model based on functional networks whose accuracy of fitting and forecasting is more than some traditional methods has some value about theory and application.