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

Socpus ID

79952589387 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79952589387

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