000 03959nam a22005055i 4500
001 978-1-84628-069-6
003 DE-He213
005 20160302161520.0
007 cr nn 008mamaa
008 100301s2005 xxk| s |||| 0|eng d
020 _a9781846280696
_9978-1-84628-069-6
024 7 _a10.1007/b138169
_2doi
050 4 _aQA76.9.C65
072 7 _aUGK
_2bicssc
072 7 _aCOM072000
_2bisacsh
082 0 4 _a003.3
_223
100 1 _aPassino, Kevin M.
_eauthor.
245 1 0 _aBiomimicry for Optimization, Control, and Automation
_h[electronic resource] /
_cby Kevin M. Passino.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _aXXXI, 926 p. 365 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChallenges 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 _aBiomimicry 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 _aComputer science.
650 0 _aComputer simulation.
650 0 _aMathematical optimization.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 0 _aElectrical engineering.
650 1 4 _aComputer Science.
650 2 4 _aSimulation and Modeling.
650 2 4 _aOptimization.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aCommunications Engineering, Networks.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852338046
856 4 0 _uhttp://dx.doi.org/10.1007/b138169
912 _aZDB-2-SCS
999 _c173621
_d173621