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Fluctuation-Induced Network Control and Learning

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Cover of 'Fluctuation-Induced Network Control and Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Yuragi Theory and Yuragi Control
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    Chapter 2 Functional Roles of Yuragi in Biosystems
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    Chapter 3 Next-Generation Bio- and Brain-Inspired Networking
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    Chapter 4 Yuragi-Based Virtual Network Control
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    Chapter 5 Introduction to Yuragi Learning
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    Chapter 6 Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing
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    Chapter 7 Application to IoT Network Control: Predictive Network Control Based on Real-World Information
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    Chapter 8 Another Prediction Method and Application to Low-Power Wide-Area Networks
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    Chapter 9 Artificial Intelligence Platform for Yuragi Learning
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    Chapter 10 Bias-Free Yuragi Learning
Attention for Chapter 6: Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing
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Summary
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Chapter title
Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing
Chapter number 6
Book title
Fluctuation-Induced Network Control and Learning
Published by
Springer, Singapore, November 2020
DOI 10.1007/978-981-33-4976-6_6
Book ISBNs
978-9-81-334975-9, 978-9-81-334976-6
Authors

Onur Alparslan, Shin’ichi Arakawa, Alparslan, Onur, Arakawa, Shin’ichi

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