Absolute Exponential Stability of A Class of Continuous-Time Recurrent Neural Networks  

Oleh:

Hu, Sanqing ; Jun, Wang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 1 (Jan. 2003), page 35-45.
Topik: time; absolute; exponential stability; continuous - time; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.8 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents a new result on absolute exponential stability (AEST) of a class of continuous - time recurrent neural networks with locally Lipschitz continuous and monotone nondecreasing activation functions. The additively diagonally stable connection weight matrices are proven to be able to guarantee AEST of the neural networks. The AEST result extends and improves the existing absolute stability and AEST ones in the literature.

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