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Robust single-hidden layer feedforward network-based pattern classifier

journal contribution
posted on 2012-12-01, 00:00 authored by Z Man, K Lee, D Wang, Z Cao, Sui Yang KhooSui Yang Khoo
In this paper, a new robust single-hidden layer feedforward network (SLFN)-based pattern classifier is developed. It is shown that the frequency spectrums of the desired feature vectors can be specified in terms of the discrete Fourier transform (DFT) technique. The input weights of the SLFN are then optimized with the regularization theory such that the error between the frequency components of the desired feature vectors and the ones of the feature vectors extracted from the outputs of the hidden layer is minimized. For the linearly separable input patterns, the hidden layer of the SLFN plays the role of removing the effects of the disturbance from the noisy input data and providing the linearly separable feature vectors for the accurate classification. However, for the nonlinearly separable input patterns, the hidden layer is capable of assigning the DFTs of all feature vectors to the desired positions in the frequencydomain such that the separability of all nonlinearly separable patterns are maximized. In addition, the output weights of the SLFN are also optimally designed so that both the empirical and the structural risks are well balanced and minimized in a noisy environment. Two simulation examples are presented to show the excellent performance and effectiveness of the proposed classification scheme.

History

Journal

IEEE transactions on neural networks and learning systems

Volume

23

Issue

12

Pagination

1974 - 1986

Publisher

IEEE

Location

Piscataway, N. J.

ISSN

2162-237X

eISSN

2162-2388

Language

eng

Publication classification

C1 Refereed article in a scholarly journal