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Neural network algorithm computer time

It is much less expensive in computer time than a neural network algorithm. [Pg.439]

The framework of presented intelligent multi-sensor system is reflected by its data processing flow as illustrated in Fig. 3. Diversified sensors in field and sophisticated algorithms make the system scalable and adaptive to different driving profiles and scenarios. Data sets of complementary sensors are synchronized on the same time base before being conveyed to feature computation components. Based on the outcome of feature computation selected data sets are fused on the Mature level to construct input vectors for pattern classification so as to detect driver drowsiness. The classifier being used in this work is built upon Artificial Neural Network (ANN) or, more particularly. Multilayer Perceptrons (MLP) with supervised training procedure. [Pg.126]


See other pages where Neural network algorithm computer time is mentioned: [Pg.56]    [Pg.692]    [Pg.346]    [Pg.115]    [Pg.232]    [Pg.168]    [Pg.198]    [Pg.222]    [Pg.295]    [Pg.2406]    [Pg.120]    [Pg.364]    [Pg.405]    [Pg.332]    [Pg.232]    [Pg.160]    [Pg.107]    [Pg.398]    [Pg.18]    [Pg.215]    [Pg.2039]    [Pg.55]    [Pg.240]    [Pg.352]    [Pg.390]    [Pg.465]    [Pg.219]    [Pg.220]    [Pg.287]    [Pg.536]    [Pg.540]    [Pg.558]    [Pg.1305]    [Pg.257]    [Pg.1521]    [Pg.271]    [Pg.499]    [Pg.193]    [Pg.60]    [Pg.87]    [Pg.49]    [Pg.577]    [Pg.1894]   
See also in sourсe #XX -- [ Pg.439 ]




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