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首页> 外文期刊>British Journal of Mathematics Computer Science >Multiperiodicity Evoked by Periodic External Inputs inCohen-Grossberg-type BAM Networks with Discrete andDistributed Delays
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Multiperiodicity Evoked by Periodic External Inputs inCohen-Grossberg-type BAM Networks with Discrete andDistributed Delays

机译:具有离散和分布时滞的Cohen-Grossberg型BAM网络中周期性外部输入引起的多周期

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摘要

In this paper, by using contraction mapping theorem, analysis approach and decomposition of solution space, the multiperiodicity issue is discussed for Cohen-Grossberg-type (CG-type) bidirectional associative memory networks (BAMNs) with discrete and distributed delays and a general class of activation functions, where the general class of activation functions consist of nondecreasing functions with saturations including piecewise linear functions with two corner points and standard activation functions as their special cases. It is shown that for any saturation region, if there is a periodic orbit located in it, it must be locally exponentially stable. Then, based on this result, some conditions are derived for ascertaining the (n + m)-neuron CG-type BAMNs can have 2 locally exponentially stable limit cycles located in two saturation regions respectively which are symmetrical. Also, taking account of different saturation regions, results about 2(p + q) (p ≤ m, q ≤ n-1), 2min{n,m} locally exponentially stable limit cycles can be obtained, where n is the number of the neurons in one layer, m is the number of the neurons in the other layer. Meanwhile, for every locally exponentially stable limit cycle given, the corresponding saturation region can be expressed concretely. Finally, three examples are given to illustrate the effectiveness of the obtained results.
机译:本文通过使用压缩映射定理,分析方法和解空间分解,讨论了具有离散和分布式时滞的Cohen-Grossberg型(CG型)双向联想存储网络(BAMN)的多周期问题和一个通用类。其中,激活函数的一般类别包括饱和的非递减函数,包括具有两个角点的分段线性函数和特殊情况下的标准激活函数。结果表明,对于任何饱和区域,如果其中存在一个周期性轨道,它就必须是局部指数稳定的。然后,基于此结果,得出一些条件来确定(n + m)-神经元CG型BAMN可以具有分别位于两个对称的饱和区域中的2个局部指数稳定极限环。同样,考虑到不同的饱和区域,结果约为2(p + q)(p≤m,q≤n-1),可得到2min {n,m}局部指数稳定极限环,其中n是一层中的神经元,m是另一层中的神经元数。同时,对于给定的每个局部指数稳定极限环,可以具体表示相应的饱和区域。最后,给出了三个例子来说明所获得结果的有效性。

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