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Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data 被引量:4
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作者 MARTINEZ-MORALES jose QUEJ-COSGAYA Hector +2 位作者 lagunas-jimenez jose PALACIOS-HERNANDEZ Elvia MORALES-SALDANA Jorge 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2019年第6期1055-1064,共10页
A multilayer perceptron(MLP) artificial neural network(ANN) model has been optimized by the multi-objective ant colony optimization(MOACO) algorithm, which uses three objective functions. A sensitivity analysis to cho... A multilayer perceptron(MLP) artificial neural network(ANN) model has been optimized by the multi-objective ant colony optimization(MOACO) algorithm, which uses three objective functions. A sensitivity analysis to choose MOACO parameter values is carried out by calculating hypervolume metric, and the proposed approach adopts the Vlsekriterijumska Optimizacija I Kompromisno Resenje(VIKOR) decision method to choose final compromised solution on the Pareto front obtained from MOACO. As a result, we used the MLP-MOACO developed model to estimate the value of engine emissions of NOxin a four stroke, spark ignition(SI) gasoline engine and observed acceptable correlation coefficient(R^2) of 0.99978. 展开更多
关键词 ANT COLONY optimization MULTILAYER PERCEPTRON artificial NEURAL networks hypervolume engine EMISSIONS
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