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The use of networks to represent the interconnections allows designers to focus on the underlying relationships input into the system rather than just the resultant layouts.
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An ANN model is proposed to observe relationships between input and output parameters by learning from a number of input patterns and their associated output patterns.
ANNs are capable of identifying complex, nonlinear relationships between input and output datasets.
Here, we consider the basic relationships between input and output signals in a quadrature detector.
For discrete attributes, the algorithm makes predictions based on the relationships between input columns in a dataset.
ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output datasets.
Artificial neural networks (ANN) are very powerful in extracting non-linear and complex relationships between input and output patterns.
Without any prior, SNNs learn to compute a non-linear metric using only similarity and dissimilarity relationships between input data.
Computer simulations are often used to replace physical experiments for exploring the complex relationships between input and output variables.
At the nanoscale, there are often very complex relationships among input design parameters and process or product outputs.
Hollywood adds that "the places where really sophisticated data mining algorithms shine" are those where "there are very complex nonlinear relationships between input data and output data".
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