Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation

• Published in: Journal of physics. Conference series 2017-12, Vol.930 (1), p.12025
• Main Authors: Hardinata, Jaya Tata, Zarlis, Muhammad, Nababan, Erna Budhiarti, Hartama, Dedy, Sembiring, Rahmat W
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Back propagation; Back propagation networks; Computer architecture; Learning; Machine learning; Mathematical models; Physics; Process parameters; Training
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/930/1/012025

One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).