Improved Conditions for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays  

Oleh:

Zeng, Zhigang ; Jun, Wang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 623-635.
Topik: NEURAL NETWORKS; conditions; global exponential; stability; neural network; time varying delays

Ketersediaan

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

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Isi artikel

This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time - varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail.

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