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The gray Verhulst model has the extremely widespread application in the study of minority, poor information and uncertainty question when the data show saturated state or s-shaped sequences. However the gray Verhulst model built by weakening the randomness of data sequence, lacking of self-organizing and self-learning. Some scholars study on this issue, and put forward a kind of gray Verhulst-BPNN...
The dimensional shrinkage for single-layer flexible printed circuit (FPC) panels measured after post-lamination baking has been evaluated experimentally and using a finite element model. Experiments and modelling for dot hatch flexible printed circuit panels show a linearly decreasing dimensional change with increasing copper fill percentage. Across three material suppliers, there is a strong variation...
The good performances of most classical learning algorithms are generally founded on high quality training data, which are clean and unbiased. The availability of such data is however becoming much harder than ever in many real world problems due to the difficulties in collecting large scale unbiased data and precisely labeling them for training. In this paper, we propose a general Contrast Co-learning...
Combinatorial testing is an important approach to detecting interaction errors for a system with several parameters. Existing research in this area assumes that all parameters of the system under test are always effective. However, in many realistic applications, there may exist some parameters that can disable other parameters in certain conditions. These parameters are called shielding parameters...
Model predictive control (MPC) is a widely used control scheme that handles constraints directly. In practice, the initial performance of MPC is usually satisfactory after a careful setup stage. However, over time, physical changes in the plant may invalidate the predictive model used in MPC and control performance degrades. Thus at least a model update is needed to restore the plant performance....
This paper discusses the method of modeling for hydraulic retarder internal pressure and determines a method based on artificial neural network (ANN). For modeling based on ANN, the detailed settings and key points are expatiated. Through data extrapolating and comparison with test data, the rationality of ANN internal pressure model is proved.
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