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More abundant output patterns make the learning algorithm more efficient.
We analyse the input and output patterns of a single-bit full adder circuit.
The improved UGF method can effectively calculate the system reliability over different time and specific wind power output patterns.
Artificial neural networks (ANN) are very powerful in extracting non-linear and complex relationships between input and output patterns.
The sequence reconstruction properties are determined by the corresponding channel output patterns given by the signs at the sampling instances.
Neural networks are quantitative models which learn to associate input and output patterns adaptively with the use of learning algorithms.
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It is one of the famous prediction models as it has the remarkable ability of mapping complex and highly non-linear input-output patterns without the knowledge of the actual model structure.
Limited diffusion within the broader context of input-output patterns leads to the second key point.
Observed input-output patterns often differ from the simple Michaelis-Menten pattern in Figure 1.
The connection between diffusion and system input-output patterns has received relatively little attention.
By contrast, most observed input-output patterns have higher Hill coefficients.
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