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2017 | OriginalPaper | Buchkapitel

8. A Recurrent Neural Fuzzy Network

verfasst von : Iuliana F. Iatan

Erschienen in: Issues in the Use of Neural Networks in Information Retrieval

Verlag: Springer International Publishing

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Abstract

Besides the feedforward neural networks, there are the recurrent networks, where the impulses can be transmitted in both directions due to some reaction connections in these networks. Recurrent Neural Networks (RNNs) are linear or nonlinear dynamic systems. The dynamic behavior presented by the recurrent neural networks can be described both in continuous time, by differential equations and at discrete times by the recurrence relations (difference equations). The distinction between recurrent (or dynamic) neural networks and static neural networks is due to recurrent connections both between the layers of neurons of these networks and within the same layer, too. The aim of this chapter is to describe a Recurrent Fuzzy Neural Network (RFNN) model, whose learning algorithm is based on the Improved Particle Swarm Optimization (IPSO) method.

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Titel: A Recurrent Neural Fuzzy Network
verfasst von: Iuliana F. Iatan
Verlag: Springer International Publishing
Buch: Issues in the Use of Neural Networks in Information Retrieval
Print ISBN: 978-3-319-43870-2

Electronic ISBN: 978-3-319-43871-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-43871-9_8

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