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<p>In this paper, a new projection neural network (PNN) is proposed for the convex nonlinear second-order cone programming (CNSOCP) with linear constraints. The KKT system of the CNSOCP with linear constraints is equivalent to the cone projection equation. Based on the natural residual function, a new PNN is proposed by using the descent direction of the Lyapunov function. The new PNN is derived from a projection descent direction of the prediction-correction projection and contraction method. Furthermore, the Lyapunov stability and global convergence of the proposed PNN are proved. The numerical results show the new PNN is efficient for CNSOCP problems with linear constraints and two force optimization problems.</p>

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A new projection neural network for convex nonlinear second-order cone programming with an application in force optimization problems

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Xuewen Mu,
  • Bingcong Zhao,
  • Yitong Liu

摘要

In this paper, a new projection neural network (PNN) is proposed for the convex nonlinear second-order cone programming (CNSOCP) with linear constraints. The KKT system of the CNSOCP with linear constraints is equivalent to the cone projection equation. Based on the natural residual function, a new PNN is proposed by using the descent direction of the Lyapunov function. The new PNN is derived from a projection descent direction of the prediction-correction projection and contraction method. Furthermore, the Lyapunov stability and global convergence of the proposed PNN are proved. The numerical results show the new PNN is efficient for CNSOCP problems with linear constraints and two force optimization problems.