Artificial neural network models

Peter Tino*, Lubica Benuskova, Alessandro Sperduti

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter

    15 Citations (Scopus)

    Abstract

    We outline the main models and developments in the broad field of artificial neural networks (ANN). A brief introduction to biological neurons motivates the initial formal neuron model - the perceptron. We then study how such formal neurons can be generalized and connected in network structures. Starting with the biologically motivated layered structure of ANN (feed-forward ANN), the networks are then generalized to include feedback loops (recurrent ANN) and even more abstract gen-eralized forms of feedback connections (recursive neuronal networks) enabling processing of structured data, such as sequences, trees, and graphs. We also introduce ANN models capable of forming topographic lower-dimensional maps of data (self-organizing maps). For each ANN type we out-line the basic principles of training the corresponding ANN models on an appropriate data collection.

    Original languageEnglish
    Title of host publicationSpringer Handbook of Computational Intelligence
    EditorsJanusz Kacprzyk , Witold Pedrycz
    PublisherSpringer
    Pages455-471
    Number of pages17
    ISBN (Electronic)9783662435052
    ISBN (Print)9783662435045
    DOIs
    Publication statusPublished - 1 Jan 2015

    ASJC Scopus subject areas

    • Computer Science(all)

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