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Complexity
Volume 2018, Article ID 1947250, 8 pages
https://doi.org/10.1155/2018/1947250
Research Article

The Spiral Discovery Network as an Automated General-Purpose Optimization Tool

Department of Informatics, Széchenyi István University, Győr, Hungary

Correspondence should be addressed to Adam B. Csapo; uh.ezs@mada.opasc

Received 29 September 2017; Accepted 22 January 2018; Published 12 March 2018

Academic Editor: Kevin Wong

Copyright © 2018 Adam B. Csapo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The Spiral Discovery Method (SDM) was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The behavior of the generalized SDM model, referred to as the Spiral Discovery Network (SDN), and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today.