多输出神经元模型的MFNN带正则化因子RLS算法

在神经网络的学习中，将递推最小二乘算法（RLS）与正则化因子相结合，一方面，可以提高网络的泛化能力，另一方面，对学习样本的噪声具有鲁棒性。但是，当网络规模较大时，该算法每迭代一步计算复杂度和存储量要求很大。本文将带正则化因子的RLS算法应用于多输出神经元模型的多层前向神经网络，通过仿真实验，结果表明，本方法可以大大简化网络结构，减小每迭代一步计算的复杂度和存储量。

A REGULARIZER FOR RLS ALGORITHM IN MFNN WITH MULTIOUTPUT NEURAL MODEL

Recursive least squares(RLS)-based algorithms are a class of fast online training algorithms for feedforward multilayered networks(MFNN).Regularizer can improve the generalization of the trained networks.Used RLS methods together with the regularizer,the gene-(ralization) ability and convergent speed are improved.However,this algorithm achieves better performance at the expense of much greater computational and storage requirements.In this paper,RLS with regularizer is used for training MO-MFNN.By several simulations,it is proved that the modified methods can improve computational complexity and storage requirements and the generalization ability of the networks.