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The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem

The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem

Cha-Hwa Lin, Jin-Fu Wang
Copyright: © 2010 |Pages: 24
ISBN13: 9781615207572|ISBN10: 1615207570|ISBN13 Softcover: 9781616922825|EISBN13: 9781615207589
DOI: 10.4018/978-1-61520-757-2.ch011
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MLA

Lin, Cha-Hwa, and Jin-Fu Wang. "The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem." Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies, edited by Leon Shyue-Liang Wang and Tzung-Pei Hong, IGI Global, 2010, pp. 226-249. https://doi.org/10.4018/978-1-61520-757-2.ch011

APA

Lin, C. & Wang, J. (2010). The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem. In L. Wang & T. Hong (Eds.), Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies (pp. 226-249). IGI Global. https://doi.org/10.4018/978-1-61520-757-2.ch011

Chicago

Lin, Cha-Hwa, and Jin-Fu Wang. "The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem." In Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies, edited by Leon Shyue-Liang Wang and Tzung-Pei Hong, 226-249. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-757-2.ch011

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Abstract

Mobile agent planning (MAP) is one of the most important techniques in the mobile computing paradigm to complete a given task in the most efficient manner. To tackle this challenging NP-hard problem, Hopfield-Tank neural network is modified to provide a dynamic approach which not only optimizes the cost of mobile agents in a spatio-temporal computing environment, but also satisfies the location-based constraints such as the starting and ending nodes of the routing sequence which must be the home site of the traveling mobile agent. Meanwhile, the energy function is reformulated into a Lyapunov function to guarantee the convergence to a stable state and the existence of valid solutions. Moreover, the objective function is designed to estimate the completion time of a valid solution and to predict the optimal routing path. This method can produce solutions rapidly that are very close to the minimum cost of the location-based and time-constrained distributed MAP problem.

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