6533b873fe1ef96bd12d5f6b
RESEARCH PRODUCT
Fast Convergence of Neural Networks by Application of a New Min-Max Algorithm
F. SorbelloAntonio Chellasubject
Mathematical optimizationError functionArtificial neural networkComputer scienceSimple (abstract algebra)Convergence (routing)MinimaxGradient descentdescription
Abstract The paper presents a new application of the min-max method, an original algorithm previously successfully applied in other areas and based on a combination of the quasi-Newton and steepest descent methods in order to find the weights minimising the error function of a feed forward neural networks. Preliminary results, obtained by applying the proposed method to a simple 2-2-1 architecture on small Boolean learning problems, are very promising.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 1992-01-01 |