Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76624
Title: Nonlinearly activated neural network for solving dynamic complex-valued matrix pseudoinverse
Authors: Xiang, Q
Liao, B
Long, J 
Liu, M
Zhang, Y 
Keywords: Complex-Valued
Dynamic
Li Activation Function
Neural Network
Pseudoinverse
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: 36th Chinese Control Conference, CCC 2017, Dalian, China, 26 - 28 July 2017, 8027965, p. 3888-3892 How to cite?
Abstract: A special class of recurrent neural network, termed Zhang neural network (ZNN), has been recently proposed for solving various dynamic problems, and has shown excellent performance in the real-valued domain. In this paper, a new complex-valued ZNN model (termed CVZNN model) is firstly proposed and investigated for online solution of dynamic complex-valued matrix pseudoinverse. Particularly, a novel activation function, called Li activation function, is employed, which is proven to enable the CVZNN model to converge in finite time. For comparative purposes, the linear activation function is exploited for solving such a dynamic complex-valued problem. Computer simulations are conducted to evaluate and compare the performance of CVZNN model with different activation functions for the dynamic complex-valued matrix pseudoinversion. Both theoretical analysis and simulation results verify the efficacy of the CVZNN model with nonlinear activation function.
URI: http://hdl.handle.net/10397/76624
ISBN: 9789881563934
ISSN: 1934-1768
DOI: 10.23919/ChiCC.2017.8027965
Appears in Collections:Conference Paper

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