A fast constructive learning algorithm for single-hidden-layer neural networks

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The gradient-based learning algorithms are usually used to train feedforward neural networks. In these algorithms, the parameters of the network are adjusted iteratively according to the partial gradients of the user-defined performance functions. Such algorithms usually require tens to hundreds of learning epochs to reach the required accuracy. If it sticks in the local minimum in the learning process, the situation tends to be even worse. In Huang et al., a novel fast learning algorithm called extreme learning machine (ELM) for single-hidden-layer neural networks (SLFNs) has been proposed where a constructive method is used instead of a gradient-based learning algorithm. In this paper, we further verify the performance of ELM on two benchmark artificial problems.