Open AccessArtificial Neural Networks For Pattern Recognize Handwritten
Author(s) -
Arief Kelik Nugroho,
Ipung Permadi,
Aini Hanifa,
Dian Tri Wiyanti
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/982/1/012008
Subject(s) - artificial neural network , artificial intelligence , backpropagation , time delay neural network , computer science , process (computing) , types of artificial neural networks , pattern recognition (psychology) , set (abstract data type) , deep learning , physical neural network , machine learning , programming language , operating system
Backpropagation is a supervised learning method on artificial neural networks. The process of training artificial neural networks that are built will be tested several structures of artificial neural networks, with the hope that the best structure is obtained to produce the output with the smallest error. The evaluation purposes use the same data set to form a neural model with a hidden level and the neuron model with two hidden levels then compare these three models both in terms of classification accuracy and training time. A neural network can be concluded that for each architecture, the neural network architecture used to recognize type 3 is 82,42%, type 2 77,5%, type 1 98,1%.