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A Discrete-Time Recurrent Neural Network for Solving Shortest Path problem

机译:用于解决最短路径问题的离散时间经常性神经网络

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This paper presetns a discrete-time recurrent neural network for solving the shortest path problem. The proposed discrete-time recurrent neural network is proven to be globally convergent to an exact solution. In addition, the proposed neural network has fixed design parameters adn simple architecture, thus is more suitable for hardware implementation. Furthermore, an improved network with a larger step size is proposed to increase the convergence rate. The performane and operating characteristics of the proposed neural network are demonstrated by means of simulation results.
机译:本文推定了一种离散时间经常性神经网络,用于解决最短路径问题。拟议的离散时间经常性神经网络被证明是全局收敛到精确的解决方案。此外,所提出的神经网络具有固定设计参数ADN简单架构,因此更适合硬件实现。此外,提出了一种具有更大步长的改进网络以增加收敛速率。通过仿真结果证明了所提出的神经网络的表现和操作特性。

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