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Chaotic Feature Selection and Reconstruction in Time Series Prediction Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Pattern Retrieval by Quaternionic Associative Memory with Dual Connections

Authors : Toshifumi Minemoto, Teijiro Isokawa, Masaki Kobayashi, Haruhiko Nishimura, Nobuyuki Matsui

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

An associative memory based on Hopfield-type neural network, called Quaternionic Hopfield Associative Memory with Dual Connection (QHAMDC), is presented and analyzed in this paper. The state of a neuron, input, output, and connection weights are encoded by quaternion, a class of hypercomplex number systems with non-commutativity for its multiplications. In QHAMDC, calculation for an internal state of a neuron is conducted by two types of multiplications for neuron’s output and connection weight. This makes robustness of the proposed associative memory for retrieval of patterns. The experimental results show that the performances of retrieving patterns by QHAMDC are superior to those by the previous QHAM.

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Metadata
Title
Pattern Retrieval by Quaternionic Associative Memory with Dual Connections
Authors
Toshifumi Minemoto
Teijiro Isokawa
Masaki Kobayashi
Haruhiko Nishimura
Nobuyuki Matsui
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_35

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