In this paper, we extract the core idea of state perturbation from Hopfield-type neural networks and define state perturbation formulas to describe the general way of optimization methods. Departing from the core idea and the formulas, we propose a novel optimization method related to neural network structure, named structure perturbation optimization. Our method can produce a structure transforming process to retrain Hopfield-type neural networks to get better problem-solving ability. Experiments validate that our method effectively helps Hopfield-type neural networks to escape from local minima and get superior solutions. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Yang, G., Li, X., Xu, J., & Jin, Q. (2014). Structure perturbation optimization for hopfield-type neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 307–314). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_39
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