On a Stabilization Problem of Nonlinear Programming Neural Networks

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Publicado en:Neural Processing Letters vol. 31, no. 2 (Apr 2010), p. 93
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Springer Nature B.V.
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Acceso en liña:Citation/Abstract
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245 1 |a On a Stabilization Problem of Nonlinear Programming Neural Networks 
260 |b Springer Nature B.V.  |c Apr 2010 
513 |a Journal Article 
520 3 |a Intrinsically, Lagrange multipliers in nonlinear programming algorithms play a regulating role in the process of searching optimal solution of constrained optimization problems. Hence, they can be regarded as the counterpart of control input variables in control systems. From this perspective, it is demonstrated that constructing nonlinear programming neural networks may be formulated into solving servomechanism problems with unknown equilibrium point which coincides with optimal solution. In this paper, under second-order sufficient assumption of nonlinear programming problems, a dynamic output feedback control law analogous to that of nonlinear servomechanism problems is proposed to stabilize the corresponding nonlinear programming neural networks. Moreover, the asymptotical stability is shown by Lyapunov First Approximation Principle. 
653 |a Algorithms 
653 |a Control theory 
653 |a Neural networks 
653 |a Servomechanisms 
653 |a Feedback control 
653 |a Lagrange multiplier 
653 |a Nonlinear programming 
653 |a Output feedback 
773 0 |t Neural Processing Letters  |g vol. 31, no. 2 (Apr 2010), p. 93 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2918339089/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
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