|
|
|
|
LEADER |
03976nam a22005055i 4500 |
001 |
978-1-84628-069-6 |
003 |
DE-He213 |
005 |
20151204182443.0 |
007 |
cr nn 008mamaa |
008 |
100301s2005 xxk| s |||| 0|eng d |
020 |
|
|
|a 9781846280696
|9 978-1-84628-069-6
|
024 |
7 |
|
|a 10.1007/b138169
|2 doi
|
040 |
|
|
|d GrThAP
|
050 |
|
4 |
|a QA76.9.C65
|
072 |
|
7 |
|a UGK
|2 bicssc
|
072 |
|
7 |
|a COM072000
|2 bisacsh
|
082 |
0 |
4 |
|a 003.3
|2 23
|
100 |
1 |
|
|a Passino, Kevin M.
|e author.
|
245 |
1 |
0 |
|a Biomimicry for Optimization, Control, and Automation
|h [electronic resource] /
|c by Kevin M. Passino.
|
264 |
|
1 |
|a London :
|b Springer London,
|c 2005.
|
300 |
|
|
|a XXXI, 926 p. 365 illus.
|b online resource.
|
336 |
|
|
|a text
|b txt
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
347 |
|
|
|a text file
|b PDF
|2 rda
|
505 |
0 |
|
|a Challenges in Computer Control and Automation -- Scientific Foundations for Biomimicry -- For Further Study -- Elements of Decision Making -- Neural Network Substrates for Control Instincts -- Rule-Based Control -- Planning Systems -- Attentional Systems -- For Further Study -- Learning -- Learning and Control -- Linear Least Squares Methods -- Gradient Methods -- Adaptive Control -- For Further Study -- Evolution -- The Genetic Algorithm -- Stochastic and Nongradient Optimization for Design -- Evolution and Learning: Synergistic Effects -- For Further Study -- Foraging -- Cooperative Foraging and Search -- Competitive and Intelligent Foraging -- For Further Study.
|
520 |
|
|
|a 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.
|
650 |
|
0 |
|a Computer science.
|
650 |
|
0 |
|a Computer simulation.
|
650 |
|
0 |
|a Mathematical optimization.
|
650 |
|
0 |
|a Control engineering.
|
650 |
|
0 |
|a Robotics.
|
650 |
|
0 |
|a Mechatronics.
|
650 |
|
0 |
|a Electrical engineering.
|
650 |
1 |
4 |
|a Computer Science.
|
650 |
2 |
4 |
|a Simulation and Modeling.
|
650 |
2 |
4 |
|a Optimization.
|
650 |
2 |
4 |
|a Control, Robotics, Mechatronics.
|
650 |
2 |
4 |
|a Communications Engineering, Networks.
|
710 |
2 |
|
|a SpringerLink (Online service)
|
773 |
0 |
|
|t Springer eBooks
|
776 |
0 |
8 |
|i Printed edition:
|z 9781852338046
|
856 |
4 |
0 |
|u http://dx.doi.org/10.1007/b138169
|z Full Text via HEAL-Link
|
912 |
|
|
|a ZDB-2-SCS
|
950 |
|
|
|a Computer Science (Springer-11645)
|