Exponential Convergence Estimates for a Single Neuron System of Neutral-Type Academic Article

abstract

• The future behavior of a dynamical system is determined by its initial state or initial function. Nontrivial neuron system involving adaptive learning corresponds to the memorization of initial information. In this paper, exponential estimates and sufficient conditions for the exponential stability of a single neuron system of neutral-type are studied. Of particular importance is the fact that exponential convergence guarantees that this system is capable of memorizing initial functions. Furthermore, this system is also capable of conveying much more information with respect to the initial functions memorized by neuron system with time delay. The proofs follow some new results on nonhomogeneous difference equations evolving in continuous-time combined with the Lyapunov-Krasovskii functional and the descriptor system approach. The exponential stability conditions are expressed in terms of a linear matrix inequality, which lead to less restrictive and less conservative exponential estimates. 2014 IEEE.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

author list (cited authors)

• Liao, X., Li, C., & Huang, T.

citation count

• 7

complete list of authors

• Liao, Xiaofeng||Li, Chuandong||Huang, Tingwen

publication date

• July 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• Exponential Estimates
• Linear Matrix Inequality (lmi)
• Lyapunov-krasovskii Functional
• Neuron System
• Neutral Differential Equations
• Nonhomogeneous Difference Equations

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2013.2290698

start page

• 1401

end page

• 1406

volume

• 25

issue

• 7

URL

• http://dx.doi.org/10.1109/tnnls.2013.2290698