A modified ELM algorithm for single-hidden layer feedforward neural networks with linear nodes

A modified ELM algorithm for a class of single-hidden layer feedforward neural networks (SLFNs) with linear nodes is discussed in this paper. It is seen that the input weights of the SLFN are designed such that the hidden layer performs as a preprocessor for removing the effects of the input disturb...

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Bibliographic Details
Main Authors: Zhihong Man, Lee, K., Dianhui Wang, Zhenwei Cao, Chunyan Miao
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Biological neural networks
extreme learning machine
Finite impulse response filter
Machine learning
neural networks
pre-processor
Robustness
signal classification
Signal to noise ratio
Vectors
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Summary:A modified ELM algorithm for a class of single-hidden layer feedforward neural networks (SLFNs) with linear nodes is discussed in this paper. It is seen that the input weights of the SLFN are designed such that the hidden layer performs as a preprocessor for removing the effects of the input disturbance and reducing both the structural and the empirical risks, the output weights are then trained to minimize the output error and further balance and reduce the structural and the empirical risks of the SLFN. The performance of an SLFN-based classifier trained with the proposed scheme is evaluated in the simulation section in support of the proposed scheme.
ISSN:2156-2318
2158-2297
DOI:10.1109/ICIEA.2011.5976017