A Recurrent Neural Network for Solving Nonlinear Convex Programs Subject to Linear Constraints  

Oleh:

Youshen Xia ; Jun Wang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 2 (Mar. 2005), page 379-386.
Topik: convex relation; continous methods; global convergence; linear constraints; recurrent neural networks; strictly convex programming

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we propose a recurrent neural network for solving non linear convex programming problems with linear constraints. The proposed neural network has a simpler structure and a lower complexity for implementation than the existing neural networks for solving such problems. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution within a finite time under the condition that the objective function is strictly convex. Compared with the existing convergence results, the present results do not require Lipschitz continuity condition on the objective function. Finally, examples are provided to show the applicability of the proposed neural network.

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