Nonlinear Measures : A New Approach to Exponentioal Stability Analysis for Hopfield-Type Neural Networks  

Oleh:

Qiao, Hong ; Peng, Jigen ; Xu, Zong-Ben

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 2 (2001), page 360-370.
Topik: non linear; non linear measure; approach; exponential; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

In this paper, a new concept called nonlinear measure is introduced to quantify stability of non linear systems in the way similar to the matrix measure for stability of linear systems. Based on the new concept, a novel approach for stability analysis of neural networks is developed. With this approach, a series of new sufficient conditions for global and local exponential stability of Hopfield type neural networks is presented, which generalizes those existing results. By means of the introduced non linear measure, the exponential convergence rate of the neural networks to stable equilibrium point is estimated, and, for local stability, the attraction region of the stable equilibrium point is characterized. The developed approach can be generalized to stability analysis of other general non linear systems.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali