Learning in the feed-forward random neural network: A critical review

    Authors

    M. Georgiopoulos; C. Li;T. Kocak

    Comments

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    Abbreviated Journal Title

    Perform. Eval.

    Keywords

    Random neural network; Learning; Gradient descent; Multi-layer; perceptron; Error functions; Evolutionary neural networks; ART; SVM; CART; Multi-objective optimization; PARTICLE SWARM OPTIMIZATION; MULTIPLE DATA SETS; VIDEO QUALITY; FUZZY-ARTMAP; SYNCHRONIZED INTERACTIONS; STATISTICAL COMPARISONS; MULTILAYER PERCEPTRONS; GLOBAL OPTIMIZATION; PATTERN-RECOGNITION; QUEUING-NETWORKS; Computer Science, Hardware & Architecture; Computer Science, Theory &; Methods

    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. (C) 2010 Elsevier B.V. All rights reserved.

    Journal Title

    Performance Evaluation

    Volume

    68

    Issue/Number

    4

    Publication Date

    1-1-2011

    Document Type

    Review

    Language

    English

    First Page

    361

    Last Page

    384

    WOS Identifier

    WOS:000289541800007

    ISSN

    0166-5316

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