Open AccessAn Analysis of Stability of a Class of Neutral-Type Neural Networks with Discrete Time Delays
Author(s) -
Zeynep Orman,
Sabri Arik
Publication year - 2013
Publication title -
abstract and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 56
eISSN - 1687-0409
pISSN - 1085-3375
DOI - 10.1155/2013/143585
Subject(s) - mathematics , uniqueness , homeomorphism (graph theory) , equilibrium point , exponential stability , artificial neural network , type (biology) , norm (philosophy) , class (philosophy) , stability (learning theory) , control theory (sociology) , discrete time and continuous time , mathematical analysis , differential equation , discrete mathematics , nonlinear system , control (management) , computer science , ecology , physics , quantum mechanics , machine learning , artificial intelligence , political science , law , biology , statistics
The problem of existence, uniqueness, and global asymptotic stability is considered for the class of neutral-type neural network model with discrete time delays. By employing a suitable Lyapunov functional and using the homeomorphism mapping theorem, we derive some new delay-independent sufficient conditions for the existence, uniqueness, and global asymptotic stability of the equilibrium point for this class of neutral-type systems. The obtained conditions basically establish some norm and matrix inequalities involving the network parameters of the neural system. The main advantage of the proposed results is that they can be expressed in terms of network parameters only. Some comparative examples are also given to compare our results with the previous corresponding results and demonstrate the effectiveness of the results presented
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