Table Of ContentZhi-Hong Guan · Bin Hu
Xuemin (Sherman) Shen
Introduction
to Hybrid
Intelligent
Networks
Modeling, Communication, and Control
Introduction to Hybrid Intelligent Networks
Zhi-Hong Guan • Bin Hu
Xuemin (Sherman) Shen
Introduction
to Hybrid
Intelligent Networks
Modeling, Communication, and Control
123
Zhi-HongGuan BinHu
CollegeofAutomation WuhanNationalLaboratoryFor
HuazhongUniversityofScienceand Optoelectronics
Technology HuazhongUniversityofScienceand
Wuhan,Hubei,China Technology
Wuhan,Hubei,China
Xuemin(Sherman)Shen
ElectricalandComputerEngineering
Department
UniversityofWaterloo
Waterloo,ON,Canada
ISBN978-3-030-02160-3 ISBN978-3-030-02161-0 (eBook)
https://doi.org/10.1007/978-3-030-02161-0
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Preface
Withthedevelopmentofbrainscienceandintelligentdevicesinrecentyears,neural
networksandartificialintelligencehaveneverbeenmoreencouraging.Thehuman
brainhasendowedusmarvelousabilitiessuchasperception,recognition,learning,
decision-making,andproblem-solving.Thanksalsototheconceptualandtechnical
advances in neural networks and artificial robots, the study of brain science has
been enriched with more powerful methods and stronger tools. The integration of
biological systems and man-made engineering systems starts very early and has
never been more indispensable. The advent of brain-inspired intelligence is an
authentic evidence for such integrated research. A large number of scientists and
engineers nowadays contribute to fundamental principles and discoveries in the
fields of brain science and neural networks. Nonetheless, more efforts need to be
donetodevelopafullunderstandingofwhatthebrainstructureisandhowthebrain
functions, and to further map these real structural and functional information into
designeffective,morenatural(morebrain-inspired)intelligence.
This book intends to provide some interesting research activities on hybrid
intelligent networks (HINets) that have been identified in nature and engineering,
especially in the brain. The HINets find wide application in real systems arising
from microcosm to macrocosm, from economy to society, and from biology to
artificiality. A good example of HINets is our brain, also known as the brain
network.Followingthislineofthinking,inthisbook,theHINetsrepresenttypesof
networkedsystemsthatconsistofheterogeneousnodesandlinks,hybriddynamics
or hybrid control, as well as different level of intelligence, towards artificial
intelligence. Through the study of HINets in this book, the authors would like
to convey the message of neural networks and brain science to readers, to lay
the foundation for their appreciation in multi-agent (robot) network, and most
importantly,toinspirethemtodevelopmoresystematicmethodologiesandtomake
moreinterestingdiscoveriesinthecomingdays.Includedinthisbookaredelayed
impulsiveneuralnetworks,neurodynamics,multi-agentnetworks,hybriddynamics
analysis,collectivedynamics,aswellasevent-drivencommunication,control,and
optimizationmethods.
v
vi Preface
This book is an exquisite, organized collection of original contributed chapters
that cover fundamental principles, new theories and methodologies, and potential
applications of hybrid intelligent networks. Chapter 1 introduces the background
and motivation for the study of hybrid intelligent networks. In Chap. 2, a class
of delayed impulsive Hopfield neural networks is introduced with comparison
to continuous-time Hopfield neural networks. Chapter 3 focuses on the hybrid
impulsive neural networks with interval uncertain weights. Chapter 4 introduces
the multistability of the hybrid impulsive neural networks with application to
associativememories.InChap.5,intelligentimpulsivesynchronizationandencryp-
tion algorithms are developed on the basis of random impulsive neural networks.
Chapter6focusesonthehybridmemristor-basedimpulsiveneuralnetworksandthe
neurodynamics for multisynchronization. Chapter 7 reviews the hybrid impulsive
andswitchingcontrolanditsapplicationtononlinearsystems.InChap.8,thehybrid
impulsiveandswitchingcontrolisappliedrespectivelytolinearandnonlinearmulti-
agent networks. Chapter 9 introduces the sampling event-driven control method
anditsapplicationtocontinuous-timemulti-agentnetworks.InChap.10,ahybrid
event-time-driven asynchronous algorithm is introduced and analyzed for solving
distributedoptimizationproblemsinsensornetworks.
The materials of this book are mostly adapted from the authors’ recent pub-
lications. Acknowledgements are given to IEEE, Elsevier, and all the coauthors
for reproducing these materials. The authors are extremely grateful to Professor
GuanrongChenfromCityUniversityofHongKong,ProfessorDavidJ.Hillfrom
the University of Hong Kong, Professor Xinghuo Yu from RMIT University, and
Professor Frank L. Lewis from the University of Texas at Arlington for their
efforts in these publications. The authors thank a large number of students and
our colleagues at the College of Automation, Huazhong University of Science
and Technology, and at the Department of Electrical and Computer Engineering,
University of Waterloo, who have painstakingly read and commented on all the
chapters. The authors would particularly like to thank Zhi-Wei Liu, Ming Chi,
Long Chen, and Jiayuan Yan. The authors thank Susan Lagerstrom-Fife from
Springer Nature Switzerland AG for her patience and consistent commitment and
the publisher for advice in the development of this book. For financial support,
the authors would liketothank theNational Natural Science Foundation of China
(Grant61633011,Grant61672245,Grant61473128).
Wuhan,China Zhi-HongGuan
Wuhan,China BinHu
Waterloo,ON,Canada Xuemin(Sherman)Shen
September2018
Contents
1 HybridIntelligentNetworks .............................................. 1
1.1 OneTypicalHybridIntelligentNetwork:TheBrain ............... 1
1.2 HybridNonlinearityandHybridIntelligence....................... 5
1.3 HybridIntelligentNetworkModels................................. 9
1.4 BasicConceptsandMethodologies ................................. 15
1.5 OverviewoftheBook................................................ 21
1.6 Summary.............................................................. 24
References.................................................................... 25
2 DelayedHybridImpulsiveNeuralNetworks............................ 27
2.1 BackgroundandMotivation ......................................... 27
2.2 HopfieldNeuralNetworks........................................... 29
2.3 DelayedImpulsiveHopfieldNeuralNetworks ..................... 42
2.4 EquilibriumPropertiesoftheImpulsiveHopfieldNeural
Networks.............................................................. 46
2.5 Summary.............................................................. 55
References.................................................................... 56
3 HybridImpulsiveNeuralNetworkswithInterval-Uncertain
Weights ...................................................................... 59
3.1 Motivation............................................................ 59
3.2 HybridImpulsiveNeuralNetworkswithUncertainWeights ...... 61
3.3 GlobalRobustExponentialStability................................ 80
3.4 Summary.............................................................. 86
Appendix..................................................................... 86
References.................................................................... 90
4 MultistabilityofDelayedHybridImpulsiveNeuralNetworks........ 93
4.1 NeuralNetworks,MultistabilityandAssociativeMemories....... 93
4.2 HybridImpulsiveNeuralNetworkModeling....................... 96
4.3 EquilibriumProperties............................................... 101
vii
viii Contents
4.4 SimulationsandAssociativeMemoriesExamples ................. 112
4.5 Summary.............................................................. 124
References.................................................................... 125
5 ImpulsiveNeuralNetworksTowardsImageProtection................ 127
5.1 MotivationforNeuralSynchronization ............................. 127
5.2 ProblemFormulation................................................. 129
5.3 ImpulsiveSynchronization........................................... 134
5.4 TwoCaseStudies..................................................... 142
5.5 Summary.............................................................. 151
References.................................................................... 152
6 HybridMemristor-BasedImpulsiveNeuralNetworks................. 155
6.1 Motivation............................................................ 155
6.2 HybridDynamicsModeling......................................... 158
6.3 FuzzyHybridControlfortheMultisynchronization ............... 161
6.4 MultisynchronizationAnalysis...................................... 165
6.5 IllustrativeExamples................................................. 169
6.6 Summary.............................................................. 182
Appendix..................................................................... 182
References.................................................................... 191
7 HybridImpulsiveandSwitchingControlandItsApplication
toNonlinearSystems....................................................... 195
7.1 HybridSystemandHybridControl ................................. 195
7.2 StabilityAnalysisofHybridControlSystems...................... 199
7.3 ImpulsiveControlfortheChaoticPendulumSystem.............. 206
7.4 FromImpulsiveControltoImpulsiveStability..................... 208
7.5 Summary.............................................................. 215
References.................................................................... 216
8 HybridCommunicationandControlinMulti-AgentNetworks ...... 219
8.1 Multi-AgentNetworks............................................... 219
8.2 HybridImpulsiveControlforLinearMulti-AgentNetworks...... 221
8.3 HybridControlforNonlinearMulti-AgentNetworks.............. 226
8.4 IllustrativeExamples................................................. 237
8.5 Summary.............................................................. 241
References.................................................................... 242
9 Event-DrivenCommunicationandControlinMulti-Agent
Networks..................................................................... 245
9.1 MotivationforEvent-DrivenControl ............................... 245
9.2 ProblemFormulation................................................. 247
9.3 Event-DrivenControlforMulticonsensus .......................... 251
9.4 IllustrativeExamples................................................. 257
Contents ix
9.5 Summary.............................................................. 264
Appendix..................................................................... 265
References.................................................................... 268
10 Hybrid Event-Time-Driven Communication and Network
Optimization ................................................................ 271
10.1 SensorNetworksandOptimization ................................. 271
10.2 ProblemFormulation................................................. 274
10.3 HybridEvent-Time-DrivenOptimizationAlgorithm............... 277
10.4 IllustrativeExamples................................................. 284
10.5 Summary.............................................................. 288
References.................................................................... 291
Chapter 1
Hybrid Intelligent Networks
In this chapter, a broad but self-contained overview of the terminology of hybrid
intelligentnetworkisprovided.Section1.1firstpresentsatypicalhybridintelligent
network, the human brain. It is the brain science and brain-inspired intelligence
that motivate the study of hybrid intelligent networks in this book. Section 1.2
generallyintroducesnonlinearphenomenainnatureandengineering,andthehybrid
nonlinearityandhybridintelligencearehighlighted.Thehybridintelligentnetwork
models are discussed in Sect.1.3, including hybrid dynamical systems, complex
networks,andartificialneuralnetworks.Section1.4proposesthebasicconceptsand
methodologiesinthefieldofhybridintelligentnetworksthatarewidelyusedinfor
the subsequent chapters. Section 1.5 sketches the overall organization of the book
whereeachchapterisbrieflysummarizedforanoverviewofthebook.Section1.6
concludesthechapter.
1.1 One TypicalHybrid IntelligentNetwork:The Brain
The brain is a world consisting of a number of unexplored continents and
greatstretchesofunknownterritory.
—SantiagoRamonyCajal
In an adult human brain, there are more than 1011 neurons and 1015 synapses.
In this book, the brain refers in particular to the human brain. Neurons in the
brain communicate through reciprocally dynamical interactions, which gives the
marvelous function of the brain. Different brain areas such as prefrontal cortex,
motor cortex, V1, and V2 have also been identified in the cortical network, and
contribute to different functions of the brain [1]. Computers nowadays can do
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Z.-H.Guanetal.,IntroductiontoHybridIntelligentNetworks,
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