Neurodynamics and Attractor Network for Solving Convex Nonlinear Programming Problems  

Oleh:

Leung, Yee ; Chen, Kai-Zhou ; Gao, Xing-Bao

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 6 (Nov. 2003), page 1469-1477.
Topik: Neurodynamics; neurodynamics; attractor network; convex; non linear; programming problems

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Based on a new idea of successive approximation, this paper proposes a high - performance feedback neural network model for solving convex nonlinear programming problems. Differing from existing neural network optimization models, no dual variables, penalty parameters, or Lagrange multipliers are involved in the proposed network. It has the least number of state variables and is very simple in structure. In particular, the proposed network has better asymptotic stability. For an arbitrarily given initial point, the trajectory of the network converges to an optimal solution of the convex non linear programming problem under no more than the standard assumptions. In addition, the network can also solve linear programming and convex quadratic programming problems, and the new idea of a feedback network may be used to solve other optimization problems. Feasibility and efficiency are also substantiated by simulation examples.

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