Learning with One-dimensional Inputs

  • Ovidiu CalinEmail author
Part of the Springer Series in the Data Sciences book series (SSDS)


This chapter deals with the case of a neural network whose input is bounded and one-dimensional, \(x\in [0,1]\). Besides its simplicity, this case is important from a few points of view: it can be treated elementary, without the arsenal of functional analysis (as shall we do in Chapter 9) and, due to its constructive nature, it provides an explicit algorithm for finding the network weights.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mathematics & StatisticsEastern Michigan UniversityYpsilantiUSA

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