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• W2113105772 abstract "Several neuron activation functions have been proposed (e.g., linear, binary, sigmoid) for recurrent and multilayer artificial neural networks. In this paper we present a hysteretic neuron activation function for optimization and learning. We prove Lyapunov stability of a hysteretic Hopfield neural network, and then solve a combinatorial optimization problem (i.e., the N-queen problem) using this network. We demonstrate the advantages of hysteresis by showing increased frequency of convergence to a solution, when the parameters associated with the activation function are varied." @default.
• W2113105772 created "2016-06-24" @default.
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• W2113105772 date "2002-11-27" @default.
• W2113105772 modified "2023-09-28" @default.
• W2113105772 title "The hysteretic Hopfield neural network" @default.
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• W2113105772 doi "https://doi.org/10.1109/ijcnn.1998.686023" @default.
• W2113105772 hasPublicationYear "2002" @default.
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