Abstract:In the current paper, an application of the neuroevolution of augmenting topologies (NEAT) algorithm is considered in a structural health monitoring (SHM) application. The algorithm is a variation of genetic algorithms, applied in neural networks, and has the goal of optimising both the topology and the weights and biases of a neural network model. The algorithm is applied here to an SHM problem instead of using feedforward neural networks. The algorithm is called to search for the best-fitting topology in the… Show more
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