A new dynamic strategy of Recurrent Neural Network

Author

Li, Guanzhong

Author_Institution

Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia

fYear

2009

fDate

15-17 June 2009

Firstpage

486

Lastpage

491

Abstract

Recurrent neural networks are widely used in many applications that need to store and update context information. The number of recurrent cycle is very important to many tasks in terms of accuracy and computation time. In this paper, a dynamic strategy is extended to recurrent internal symmetry neural network, and back propagation is trained for image segmentation tasks.

Keywords

backpropagation; image segmentation; recurrent neural nets; back propagation; context information; image segmentation; recurrent cycle; recurrent internal symmetry neural network; Application software; Australia; Computer networks; Computer science; Convergence; Equations; Image segmentation; Neural networks; Recurrent neural networks; Testing; back propagation; dynamic Cycle; internal symmetry; recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on

Conference_Location

Kowloon, Hong Kong

Print_ISBN

978-1-4244-4642-1

Type

conf

DOI

10.1109/COGINF.2009.5250690

Filename

5250690

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3390887