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- W161854392 abstract "Selection of response surface method with highly accurate and efficient approximation is greatly important in the engineering application. The main process was constructed to optimize the complex system based on the alternative model. The alternative approximation ability and iterative optimization effect of the polynomial response surface and neural network model for highly nonlinear problems were compared through the typical Haupt function numerical examples. Relatively speaking, the treatment effect of adaptive polynomial response surface, artificial and radial basis function neural networks are good. During the optimization iteration of complex system, better optimization efficiency will be achieved when several surrogate models are adopted at the same time by synthesizing their advantages of space fitness." @default.
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- W161854392 date "2012-10-16" @default.
- W161854392 modified "2023-09-22" @default.
- W161854392 title "Research on Complex System Design Optimization Based on Neural Network and Hybrid Intelligent Algorithm" @default.
- W161854392 doi "https://doi.org/10.1109/icecc.2012.665" @default.
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