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An improved competitive Hopfield network with inhibitive competitive activation mechanism for maximum clique problem

机译:带有抑制竞争激活机制的改进竞争Hopfield网络,用于解决最大集团问题

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摘要

In this paper, we analyze the formula of weights definition in the discrete competitive Hopfield network (DCHOM) and point out its flaw when using it to solve some special instances of maximum clique problem (MCP). Based on the analysis, we propose an improved competitive Hopfield network algorithm (ICHN). In ICHN, we introduce a flexible weight definition method which excites the competitive dynamics, and we also present an initial values setting strategy which efficiently increases the probability of finding optimal solutions. Furthermore, an inhibitive competitive activation mechanism is introduced to form a new input updating rule which reduces significantly the number of neurons with an intermediate level of activations. Our algorithm effectively overcomes the flaw of the DCHOM, and exhibits powerful solving ability for the MCP. Experiments on the benchmark problems and practical applications verify the validity of our algorithm.
机译:在本文中,我们分析了离散竞争Hopfield网络(DCHOM)中的权重定义公式,并指出了它在解决某些最大集团问题(MCP)的特殊情况时的缺陷。在此基础上,我们提出了一种改进的竞争Hopfield网络算法(ICHN)。在ICHN中,我们引入了一种灵活的权重定义方法,该方法激发了竞争动态,并且还提出了一种初始值设置策略,该策略有效地增加了找到最优解的可能性。此外,引入了抑制性竞争激活机制以形成新的输入更新规则,该规则将显着减少具有中等激活水平的神经元的数量。我们的算法有效地克服了DCHOM的缺陷,对MCP具有强大的求解能力。对基准问题和实际应用的实验证明了该算法的有效性。

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