Title :
A modified ELM algorithm for single-hidden layer feedforward neural networks with linear nodes
Author :
Man, Zhihong ; Lee, Kevin ; Wang, Dianhui ; Cao, Zhenwei ; Miao, Chunyan
Author_Institution :
Fac. of Eng. & Ind. Sci., Swinburne Univ. of Technol., Hawthorn, VIC, Australia
Abstract :
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.
Keywords :
feedforward neural nets; learning (artificial intelligence); pattern classification; SLFN-based classifier; empirical risk reduction; extreme learning machine; input disturbance effect removal; linear nodes; modified ELM algorithm; output error minimization; output weight; single-hidden layer feedforward neural network; structural risk reduction; Algorithm design and analysis; Biological neural networks; Finite impulse response filter; Machine learning; Robustness; Signal to noise ratio; Vectors; extreme learning machine; neural networks; pre-processor; signal classification;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8754-7
Electronic_ISBN :
pending
DOI :
10.1109/ICIEA.2011.5976017
