Title: Non-linear dynamic system identification using Cascaded Functional Link Artificial Neural Network
Authors: Babita Majhi, G. Panda
Addresses: Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela – 769 008, India. ' Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela – 769 008, India
Abstract: The Multilayer Artificial Neural Network (MLANN) has been employed for identification of non-linear dynamic systems. However, this scheme offers high computational complexity and yields poor identification performance particularly for non-linear dynamic systems. In this paper, we introduce a new structure known as Cascaded Functional Link Artificial Neural Network (CFLANN), derive an appropriate learning algorithm and use it for identification task. Extensive simulation study reveals that the proposed approach outperforms the existing MLANN-based method both in terms of computational complexity and response matching.
Keywords: CFLANN; cascaded functional link ANNs; artificial neural networks; nonlinear system identification; FLANN; dynamic system identification; MLANN; learning algorithms.
DOI: 10.1504/IJAISC.2009.027293
International Journal of Artificial Intelligence and Soft Computing, 2009 Vol.1 No.2/3/4, pp.223 - 237
Published online: 19 Jul 2009 *
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