LT递归神经网络求解旅行商问题研究

Discrete-Time Recurrent Neural Networks with Linear Threshold Neurons for Solving Traveling Salesman Problem

  • 摘要: 提出了一种基于LT递归神经网络的旅行商问题求解方法。采用离散型神经网络模型,先给出模型有界性和完全收敛性的证明,再给出保证网络的稳定输出解为旅行商问题有效路径的条件。在此基础上结合局部最小值逃逸方法获得较优的路径。在与基于LV递归神经网络的算法比较实验证明,该算法在总体上能获得更好的有效路径。

     

    Abstract: This paper discusses a class of discrete-time recurrent neural networks with linear threshold (LT) neurons for solving traveling salesman problem (TSP). It first addresses the boundedness and complete stability,then gives a theorem to ensure all the networks' iteration solutions to be valid solutions. We also present an algorithm based on such networks with a local escape way. Simulation results illustrate the developed method. Compared with the TSP solutions done by Lotka-Volterra (LV) neural networks, the presented method has better performance.

     

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