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- W2386495985 abstract "Neural network structures are fixed,which results in redundant connections and high computing costs.This paper presents a cooperative quantum differential evolution algorithm(CQGADE) that simultaneously optimizes the neural network structure and parameters.The quantum genetic algorithm is used to optimize the neural network structure and the number of hidden nodes,while the differential evolution algorithm is used to optimize the neural network weights.This reduces redundant neural network structures,while the amplitude-based coding method and a cooperation mechanism improve the learning efficiency,approximation accuracy,and generalization.Simulations show that this algorithm has better prediction accuracy and robustness for predicting the number of sunspots and the flow of steam turbine." @default.
- W2386495985 created "2016-06-24" @default.
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- W2386495985 date "2012-01-01" @default.
- W2386495985 modified "2023-09-23" @default.
- W2386495985 title "Cooperative quantum differential evolution algorithm based method for optimizing neural networks" @default.
- W2386495985 hasPublicationYear "2012" @default.
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