2008
DOI: 10.1016/j.jhazmat.2007.12.107
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Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

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Cited by 27 publications
(12 citation statements)
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References 23 publications
(45 reference statements)
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“…Karzynski et al [7] used GAs to optimise both the architecture and the weights of ANN for multiclass microarray classification. Taheri and Mohebbi [15] used the GA to fine-tune ANN parameters, including the number of nodes in the hidden layers, the momentum and the learning rates. Zorić and Pandžić [17] utilised the GA to find the near optimal ANN topology in the real-time lip synchronisation classification.…”
Section: Introductionmentioning
confidence: 99%
“…Karzynski et al [7] used GAs to optimise both the architecture and the weights of ANN for multiclass microarray classification. Taheri and Mohebbi [15] used the GA to fine-tune ANN parameters, including the number of nodes in the hidden layers, the momentum and the learning rates. Zorić and Pandžić [17] utilised the GA to find the near optimal ANN topology in the real-time lip synchronisation classification.…”
Section: Introductionmentioning
confidence: 99%
“…The coding length was set to 30. Each chromosome consisted of a piece of binary code of the connection weight and threshold, which are generally limited in the search space [21].…”
Section: Initial Structure Optimization By the Genetic Algorithmmentioning
confidence: 99%
“…In 2008, Mahboobeh Taheri clarified the possibility of GA‐ANN improving the efficiency of neural network algorithms by predicting the collection efficiency of venturi scrubbers. This algorithm is characterized by the use of the GA algorithm to determine the number of neurons, momentum, and learning rate in the hidden layer, thereby minimizing the time required to find the optimal architecture and parameters for ANN‐based backpropagation …”
Section: Neural Network Algorithm Efficiencymentioning
confidence: 99%