中文摘要：

根据多项式理论，构造了一种以Laguerre正交多项式作为隐层神经元激励函数的前向神经网络模型．根据标准BP算法，导出了权值修正的迭代公式（包括标量形式和矩阵形式）．区别于这种需要迭代训练获得最优权值的方法，针对该网络模型，进一步提出了一种基于伪逆的直接计算权值的方法．该权值直接确定法避免了以往的权值反复迭代训练的冗长过程．仿真结果显示其具有比传统的BP迭代法更快的计算速度，并且能够能达到更高的工作精度．

英文摘要：

Based on network is constructed. tivated by Laguerre orth polynomial curve-fitting theory, a Laguerre orthogonal basis feed-forward neural The model adopts a three-layer structure, where the hidden-layer neurons are acogonal polynomial functions. In order to obtain optimal weights, weights -updating formula is derived firstly by standard BP training method. A pseudo-inverse based method is finally proposed, which determines the network weights without lengthy iterative training. Simulation results show that the weights-directly-determined method is more efficient and effective than conventional BP iterativetraining algorithms.