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- W2366499549 abstract "To achieve appropriate characteristic parameters and build optimized prediction model in the process of engine characteristic parameters prediction,an algorithm named Particle Swarm Optimization Kernel Extreme Learning Machine(PSO-KELM) is proposed.According to the principle of being able to reflect the transformation of the engine performance,un-disassembly examination and anti-jamming,exhaust gas temperature is chosen as the characteristic parameter representing the engine operating status.The water temperature,oil temperature,gear-lever shift and rotate speed are regard as the related variable.To reduce human factor influence,the particle swarm optimization algorithm is used to choose punishment coefficient and kernel coefficient in KELM model.Experiment results show that PSO-KELM reflects higher prediction accuracy than PSO-LS-SVM and NS-KELM.A effective approach is build for engine characteristic parameters prediction by PSO-KELM." @default.
- W2366499549 created "2016-06-24" @default.
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- W2366499549 date "2014-01-01" @default.
- W2366499549 modified "2023-09-23" @default.
- W2366499549 title "Engine Characteristic Parameters Prediction based on PSO-KELM" @default.
- W2366499549 hasPublicationYear "2014" @default.
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