Title
Learning In The Feed-Forward Random Neural Network: A Critical Review
Keywords
ART; CART; Error functions; Evolutionary neural networks; Gradient descent; Learning; Multi-layer perceptron; Multi-objective optimization; Random neural network; SVM
Abstract
The Random Neural Network (RNN) has received, since its inception in 1989, considerable attention and has been successfully used in a number of applications. In this critical review paper we focus on the feed-forward RNN model and its ability to solve classification problems. In particular, we paid special attention to the RNN literature related with learning algorithms that discover the RNN interconnection weights, suggested other potential algorithms that can be used to find the RNN interconnection weights, and compared the RNN model with other neural-network based and non-neural network based classifier models. In review, the extensive literature review and experimentation with the RNN feed-forward model provided us with the necessary guidance to introduce six critical review comments that identify some gaps in the RNN's related literature and suggest directions for future research. © 2010 Elsevier B.V. All rights reserved.
Publication Date
4-1-2011
Publication Title
Performance Evaluation
Volume
68
Issue
4
Number of Pages
361-384
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.peva.2010.07.006
Copyright Status
Unknown
Socpus ID
79952589387 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/79952589387
STARS Citation
Georgiopoulos, Michael; Li, Cong; and Kocak, Taskin, "Learning In The Feed-Forward Random Neural Network: A Critical Review" (2011). Scopus Export 2010-2014. 3430.
https://stars.library.ucf.edu/scopus2010/3430