Abstract
In this chapter we use a Hopfield-type neural network to solve the following topology optimization problem. Given a set of n sites, a set of possible links, each link has an associated cost and a probability of failure, we are required to choose a subset of links such that the cost is minimized under the constraint that the network connectivity is not less than a given threshold. The network connectivity is defmed to be the probability that every pair of nodes can communicate with each other (the all-terminal reliability). The neural network consists of n×(n-1)/2 Hysteresis McCulloch-Pitts neurons. The solution of the problem corresponds to the minimum of a Lyapunov (energy) function that represents the constraints of the problem. (This chapter is based on reference [1].)
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© 2004 Springer-Verlag Berlin Heidelberg
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AboElFotoh, H.M.F. (2004). Maximizing Topology Connectivity Using a Hopfield Neural Network. In: Soft Computing in Communications. Studies in Fuzziness and Soft Computing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45090-0_4
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DOI: https://doi.org/10.1007/978-3-540-45090-0_4
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