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Proceedings Paper

Improved delay-dependent stability criteria for neutral Hopfield neural networks
Author(s): Minglei Zang; Linyao Wu; Qiuye She; Zhongyi Chu
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Paper Abstract

The neutral Hopfield neural network (NHNN) model is considered in this paper, which can be regarded as an extension of the classical Hopfield neural network (HNN). By introducing the neutral term into the classical HNN model, the inspiration and associate memory phenomenon can be well described and explained. Based on LMI techniques, the delay-dependent stability criteria of NHNN are obtained using the free-weighting matrices method, where the free weighting matrices are applied to express the relationships between the terms in the Leibniz-Newton formula. The optimization on the free weighting matrices by means of solving linear matrix inequalities (LMIs) can be employed such that the less conservative results can be obtained than that in Zhang, et al 2005. The numerical examples demonstrate that the proposed stability criterion is less conservative than the previous works.

Paper Details

Date Published: 13 October 2008
PDF: 6 pages
Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 71281R (13 October 2008); doi: 10.1117/12.806735
Show Author Affiliations
Minglei Zang, Yantai Univ. (China)
Linyao Wu, Southeast Univ. (China)
Qiuye She, Southeast Univ. (China)
Zhongyi Chu, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 7128:
Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment

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