PaperTraining a feed-forward network by feeding gradients forward rather than by back-propagation of errors
References (5)
Elementary Functions: An Algorithmic Treatment
(1966)Neural networks and related methods for classification
J. Royal Statist. Soc.
(1994)
There are more references available in the full text version of this article.
Cited by (15)
Determination of the effects of operating conditions on the output power of the inverter and the power quality using an artificial neural network
2019, Engineering Science and Technology, an International JournalCitation Excerpt :Hence, the ANN has one input layer with 6 input data, one hidden layer with 10 neurons and one output layer with 3 output data as demonstrated in Fig. 4. In ANN step of this study, feed forward back propagation (FFBP) ANN and Levenberg–Marquardt (LM) algorithm which is intensively employed in many studies were chosen due to the reliable, simple and rapid properties although several training algorithms are existing in literature [11,29–31]. In addition, Learngdm adaptation learning function, mean square error (MSE) performance function, and Tansig transfer function were preferred in this ANN simulation.
Prediction of nano etching parameters of silicon wafer for a better energy absorption with the aid of an artificial neural network
2018, Solar Energy Materials and Solar CellsFeed-forward neural network for one-to-many mappings using fuzzy sets
2004, NeurocomputingTraining a feed-forward network with incomplete data due to missing input variables
2003, Applied Soft Computing JournalA feed-forward network for input that is both categorical and quantitative
2002, Neural Networks
Copyright © 1997 Published by Elsevier B.V.