A one-layer dual neural network with a unipolar hard-limiting activation function for shortest-path routing

Authors:
Qingshan Liu;Jun Wang
Affiliations:
School of Automation, Southeast University, Nanjing, China;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

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Abstract

The shortest path problem is an archetypal combinatorial optimization problem arising in a variety of application settings. For real-time applications, parallel computational approaches such as neural computation are more desirable. This paper presents a new recurrent neural network with a simple structure for solving the shortest path problem (SPP). Compared with the existing neural networks for SPP, the proposed neural network has a lower model complexity; i.e., the number of neurons in the neural network is the same as the number of nodes in the problem. A simple lower bound on the gain parameter is derived to guarantee the finite-time global convergence of the proposed neural network. The performance and operating characteristics of the proposed neural network are demonstrated by means of simulation results.