前馈网络的一种高精度鲁棒在线贯序学习算法

基于离散傅里叶变换-极限学习机(DFT-ELM)提出了一种新的单隐层前馈神经网络在线贯序学习算法,命名为"在线贯序-离散傅里叶变换-极限学习机"(OS-DFT-ELM).该算法能够逐个或逐段学习数据,随着新数据的逐渐到达,单隐层前馈神经网络的内权矩阵和外权矩阵得到逐步调整.该算法与在线贯序-极限学习机(OS-ELM)相比,具有更高的精度和鲁棒性.同时,通过实验和分析,表明OS-DFT-ELM具有优良性能.

An Accurate and Robust Online Sequential Learning Algorithm for Feedforward Networks

Abstract： In this paper, a kind of accurate and robust online sequential learning algorithm was proposed for single hidden layer feedforward networks. The algorithm is referred to as online sequential discrete Fourier transform extreme learning machine (OS DFT ELM). This approach is able to learn data one by one or chunk by chunk. During the growth of the data, input weights and output weights are adjusted incrementally. The proposed algorithm has a higher degree of accuracy and robustness compared to the approach referred to as online sequential extreme learning machine (OS ELM). Two simulation examples were presented to show the excellent performance of the proposed approach.