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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2019

26.04.2017 | Original Article

Dissipativity analysis of complex-valued BAM neural networks with time delay

verfasst von: C. Rajivganthi, F. A. Rihan, S. Lakshmanan

Erschienen in: Neural Computing and Applications | Ausgabe 1/2019

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Abstract

This paper is concerned with dissipativity analysis of complex-valued bidirectional associative memory (BAM) neural networks (NNs) with time delay. Some novel sufficient conditions that guarantee the dissipativity of complex-valued BAM neural networks (CVBNNs) are obtained by using the inequality techniques, Halanay inequality, and upper right Dini derivative concepts. The complex-valued nonlinear function is separated into its real and imaginary parts to a set of sufficient conditions for the global dissipativity of CVBNNs by using the matrix measure method. Moreover, the global attractive sets are obtained, which are positive invariant sets. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed theoretical results.
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Fußnoten
1
Markovian jump systems are the combination of two components: the continuous time finite state Markovian process(refers to mode) and the system of differential equations (refers to state). Markovian jump systems is described as a specialclass of dynamical systems with finite mode operation due to random changes in their structure, such as component repairsor failures, changing subsystems inter connections, sudden environmental disturbance, and so on.
 
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Metadaten
Titel
Dissipativity analysis of complex-valued BAM neural networks with time delay
verfasst von
C. Rajivganthi
F. A. Rihan
S. Lakshmanan
Publikationsdatum
26.04.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2985-9

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