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The Spiral Discovery Network as an Automated General-Purpose Optimization Tool
2018
Complexity
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
doi:10.1155/2018/1947250
fatcat:pc2mmuxi4jgwfdqpl3lukas32i