基于雙層遞歸神經(jīng)網(wǎng)絡(luò)的路由優(yōu)化算法
Routing Algorithm Based on Two-layer Recurrent Neural Network
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摘要: 研究分組交換網(wǎng)的路由選擇及流量分配問題，以網(wǎng)絡(luò)的平均時延為優(yōu)化目標函數(shù)。為使問題的解能實時、可靠地完成，將一種用于最短路徑計算的雙層遞歸神經(jīng)網(wǎng)絡(luò)應(yīng)用于路由選擇的流量導(dǎo)數(shù)法中。仿真結(jié)果表明，該算法在收斂的可靠性和計算的實時性方面有所提高。Abstract: This paper is concerned with the optimum routing and flow assignment problem in packet-switch networks. The optimum objective function is the network-wide average time delay. To make the solution be implemented reliably in real time, a neural network for shortest path computation that is a two-layer recurrent structure is applied to flow deviation method. Simulation results show that improvements can be achieved in the reliability of successful convergence and in the decrease of computation time.
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