A new collaborate neuro-dynamic framework for solving convex second order cone programming problems with an application in multi-fingered robotic hands

  • Alireza NazemiEmail author


A neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory and Lyapunov stability theory to solve convex second-order cone programming (CSOCP) problems. According to Karush-Kuhn-Tucker conditions of convex optimization, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the CSOCP problem. By employing Lyapunov function approach, it is also shown that the presented neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original optimization problem. Simulation results show that the neural network is feasible and efficient.


Neural network Second-order cone programming Convex programming Convergence Stability 



This study was not funded by any grant.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Mathematical SciencesShahrood University of TechnologyShahroodIran

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