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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Generalized Single-Hidden Layer Feedforward Networks for Regression Problems
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Generalized Single-Hidden Layer Feedforward Networks for Regression Problems

机译：回归问题的广义单隐层前馈网络

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摘要

In this paper, traditional single-hidden layer feedforward network (SLFN) is extended to novel generalized SLFN (GSLFN) by employing polynomial functions of inputs as output weights connecting randomly generated hidden units with corresponding output nodes. The significant contributions of this paper are as follows: 1) a primal GSLFN (P-GSLFN) is implemented using randomly generated hidden nodes and polynomial output weights whereby the regression matrix is augmented by full or partial input variables and only polynomial coefficients are to be estimated; 2) a simplified GSLFN (S-GSLFN) is realized by decomposing the polynomial output weights of the P-GSLFN into randomly generated polynomial nodes and tunable output weights; 3) both P- and S-GSLFN are able to achieve universal approximation if the output weights are tuned by ridge regression estimators; and 4) by virtue of the developed batch and online sequential ridge ELM (BR-ELM and OSR-ELM) learning algorithms, high performance of the proposed GSLFNs in terms of generalization and learning speed is guaranteed. Comprehensive simulation studies and comparisons with standard SLFNs are carried out on real-world regression benchmark data sets. Simulation results demonstrate that the innovative GSLFNs using BR-ELM and OSR-ELM are superior to standard SLFNs in terms of accuracy, training speed, and structure compactness.
机译：在本文中，通过将输入的多项式函数作为输出权重，将随机生成的隐藏单元与相应的输出节点相连接，将传统的单隐藏层前馈网络（SLFN）扩展为新颖的广义SLFN（GSLFN）。本文的重要贡献如下：1）使用随机生成的隐藏节点和多项式输出权重实现原始GSLFN（P-GSLFN），从而通过全部或部分输入变量来扩充回归矩阵，并且仅将多项式系数设为估计2）通过将P-GSLFN的多项式输出权重分解为随机生成的多项式节点和可调输出权重来实现简化的GSLFN（S-GSLFN）； 3）如果通过岭回归估计器调整输出权重，则P-和S-GSLFN都可以实现通用逼近；以及4）通过开发的批处理和在线顺序岭ELM（BR-ELM和OSR-ELM）学习算法，在泛化和学习速度方面保证了所提出的GSLFN的高性能。在真实世界的回归基准数据集上进行了全面的仿真研究，并与标准SLFN进行了比较。仿真结果表明，使用BR-ELM和OSR-ELM的创新型GSLFN在准确性，训练速度和结构紧凑性方面均优于标准SLFN。

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