Resumen de A fast algorithm for inversion of MLP networks in design problems

Davide Cherubini, Alessandra Fanni, Augusto Montisci, Pietro Testoni

• Purpose – To present a neural network‐based approach to the design of electromagnetic devices.

  Design/methodology/approach – A neural model is created which reproduces the relationship between the design parameters of the device and the performance parameters, typically field values.

  Findings – The neural model is a single hidden layer MLP network, trained by using a set of cases calculated, for example, by means of a finite element analysis. The design problem can be solved by fixing the performance values at the output of the network and by calculating the corresponding input values. The relationship between the input and the output of the neural network is represented by three equations systems. By means of these three systems, we can forward the domain of the input, and we can back propagate the desired output throughout the network layers. In such a way, both the domain of the design parameters and the domain of the desired performances values can be projected in the same space. Whatever point inside the intersection between the two projected domains corresponds to a solution of the design problem.

  Originality/value – Presents a procedure which is able to find a point belonging to such an intersection.