Biomimicry for Optimization, Control, and Automation

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Springer Science & Business Media, Dec 6, 2005 - Computers - 926 pages
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Biomimicry uses our scienti?c understanding of biological systems to exploit ideas from nature in order to construct some technology. In this book, we focus onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of biological systems for the development of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated levels of automation. The primary focus is not on the modeling, emulation, or analysis of some biological system. The focus is on using “bio-inspiration” to inject new ideas, techniques, and perspective into the engineering of complex automation systems. There are many biological processes that, at some level of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose automatic control, decision making, or automation. For instance, at the level of everyday experience, we can view the actions of a human operator of some process (e. g. , the driver of a car) as being a series of the best choices he or she makes in trying to achieve some goal (staying on the road); emulation of this decision-making process amounts to modeling a type of biological optimization and decision-making process, and implementation of the resulting algorithm results in “human mimicry” for automation. There are clearer examples of - ological optimization processes that are used for control and automation when you consider nonhuman biological or behavioral processes, or the (internal) - ology of the human and not the resulting external behavioral characteristics (like driving a car). For instance, there are homeostasis processes where, for instance, temperature is regulated in the human body.
 

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Contents

Chapter Contents
9
Scientific Foundations for Biomimicry
56
Chapter Contents
59
For Further Study
95
Attentional Systems 263
103
Neural Network Substrates for Control Instincts
105
Chapter Contents
107
tron
126
Adaptive Control
547
Chapter Contents
549
For Further Study
601
The Genetic Algorithm 613
608
Chapter Contents
615
Stochastic and Nongradient Optimization for Design 647
646
Chapter Contents
649
Synergistic Effects
718

RuleBased Control
153
Chapter Contents
155
Planning Systems 225
207
Planning Systems
224
Chapter Contents
227
Attentional Systems 263
262
Chapter Contents
265
For Further Study
309
Linear Least Squares Methods 421
420
Chapter Contents
423
Gradient Methods
470
Chapter Contents
475
Chapter Contents
721
3
735
For Further Study 755
754
Cooperative Foraging and Search 765
762
Chapter Contents
768
Competitive and Intelligent Foraging
829
Chapter Contents
831
For Further Study
895
BIBLIOGRAPHY
899
INDEX
922
Copyright

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About the author (2005)

JEFFREY T. SPOONER is a senior member of the technical staff at Sandia National Laboratories, Albuquerque, New Mexico.

MANFREDI MAGGIORE is an assistant professor in the Department of Electrical and Computer Engineering at the University of Toronto, Canada.

RAUL ORDO?EZ is an assistant professor in the Department of Electrical and Computer Engineering at the University of Dayton, Ohio.

KEVIN M. PASSINO is a professor in the Department of Electrical Engineering at The Ohio State University.

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