Title: Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation
Abstract: This paper presents a new parallel architecture of the Leven-berg-Marquardt (LM) algorithm for training fully connected feedforward neural networks, which will also work for MLP but some cells will stay empty. This approach is based on a very interesting idea of learning neural networks without error backpropagation. The presented architecture is based on completely new parallel structures to significantly reduce a very high computational load of the LM algorithm. A full explanation of parallel three-dimensional neural network learning structures is provided.
Publication Year: 2017
Publication Date: 2017-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 14
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot