【摘 要】
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本文用神经网络来求解多元函数的极值.针对 Hopfield 网络的局限,提出了一种高阶反馈型神经网络,并从数学上证明了这个网络具有良好的稳定性.当阶数为2时,功能类似于 Hopfiel
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本文用神经网络来求解多元函数的极值.针对 Hopfield 网络的局限,提出了一种高阶反馈型神经网络,并从数学上证明了这个网络具有良好的稳定性.当阶数为2时,功能类似于 Hopfield 网络,但比它稳定.本文还给出求解多元高次函数全局最优解的方法.最后给出了该网络的计算机数值仿真.
In this paper, neural network is used to solve the extremum of multivariate function.For the limitation of Hopfield network, a high-order feedback neural network is proposed and the network is proved to be of good stability mathematically.When the order is 2, The function is similar to that of Hopfield network, but it is more stable than that of Hopfield network.In this paper, a method to solve the global optimal solution of multivariate high order functions is given. Finally, the numerical simulation of the network is given.
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