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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes
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Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes

机译：隐含节点自适应增长的极限学习机通用逼近

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摘要

Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue $p$-integrable hidden activation functions can approximate any Lebesgue $p$-integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.
机译：极端学习机（ELM）已被提出用于一般的单隐藏层前馈网络，该网络不必像神经元，并且在回归和分类应用中都表现良好。在本文中，我们提出了一种具有自适应增长的隐藏节点的ELM（AG-ELM），它为网络的自动化设计提供了一种新方法。与其他增量式ELM（I-ELM）不同，在现有的隐藏式节点被逐个添加新的隐藏式节点时，现有的隐藏式节点被冻结，在AG-ELM中，在现有网络可以被具有更少隐藏节点和更好的泛化性能的新生成的网络所取代。然后，我们证明使用Lebesgue $ p $可集成的隐藏激活函数的AG-ELM可以在紧凑输入集上近似任何Lebesgue $ p $可集成函数。仿真结果证明并验证了这种新方法可以实现比I-ELM更紧凑的网络体系结构。

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