A New Approach for Estimating the Attraction Domain for Hopfield-Type Neural Networks
A New Approach for Estimating the Attraction Domain for Hopfield-Type Neural Networks
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DOI：
10.1162/neco.2009.11-07-637
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发表时间：
2009
影响因子：
2.9
通讯作者：
Dequan Jin;Jigen Peng
中科院分区：
文献类型：
--
作者：
Dequan Jin;Jigen Peng
本文利用E.Kaslik、St.Balint等人提出的方法，提出了一种估计Hopfield型神经网络渐近稳定平衡点的吸引域的新方法--展开法。我们从理论上和数值上证明了该方法的可行性和有效性。应用实例，包括E.Kaslik，L.Bresu和St.Balint所考虑的网络系统的数值结果表明，所提出的方法能够获得更好的吸引域估计。
In this letter, using methods proposed by E. Kaslik, St. Balint, and their colleagues, we develop a new method, expansion approach, for estimating the attraction domain of asymptotically stable equilibrium points of Hopfield-type neural networks. We prove theoretically and demonstrate numerically that the proposed approach is feasible and efficient. The numerical results that obtained in the application examples, including the network system considered by E. Kaslik, L. Brescu, and St. Balint, indicate that the proposed approach is able to achieve better attraction domain estimation.