Incremental extreme learning machine with fully complex hidden nodes
| Autor: | Guang-Bin Huang, Ming-Bin Li, Chee-Kheong Siew, Lei Chen |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Artificial neural network
business.industry Cognitive Neuroscience Activation function Domain (mathematical analysis) Computer Science Applications Nonlinear system Function approximation Physics::Plasma Physics Artificial Intelligence Bounded function Piecewise Artificial intelligence business Algorithm Extreme learning machine Mathematics |
| Zdroj: | Neurocomputing. 71:576-583 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2007.07.025 |
| Popis: | Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] has recently proposed an incremental extreme learning machine (I-ELM), which randomly adds hidden nodes incrementally and analytically determines the output weights. Although hidden nodes are generated randomly, the network constructed by I-ELM remains as a universal approximator. This paper extends I-ELM from the real domain to the complex domain. We show that, as long as the hidden layer activation function is complex continuous discriminatory or complex bounded nonlinear piecewise continuous, I-ELM can still approximate any target functions in the complex domain. The universal capability of the I-ELM in the complex domain is further verified by two function approximations and one channel equalization problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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