Existence and Uniform Stability Analysis of Fractional-Order Complex-Valued Neural Networks With Time Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 1; pp. 84 - 97
• Main Authors: Rakkiyappan, R., Jinde Cao, Velmurugan, G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Banach contraction fixed point theorem; Biological neural networks; complex-valued neural networks; Computer simulation; Constants; Delay effects; fractional order; Humans; Joints; Linear Models; Mathematical analysis; Mathematical models; Mathematics; Models, Theoretical; Neural networks; Neural Networks (Computer); Recurrent neural networks; Stability analysis; Time delay; time delays; Time Factors; complex-valued neural networks; Banach contraction fixed point theorem; time delays; fractional order
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2014.2311099

This paper deals with the problem of existence and uniform stability analysis of fractional-order complex-valued neural networks with constant time delays. Complex-valued recurrent neural networks is an extension of real-valued recurrent neural networks that includes complex-valued states, connection weights, or activation functions. This paper explains sufficient condition for the existence and uniform stability analysis of such networks. Three numerical simulations are delineated to substantiate the effectiveness of the theoretical results.