000 | 03959nam a22005055i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/b138169 _2doi |
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050 | 4 | _aQA76.9.C65 | |
072 | 7 |
_aUGK _2bicssc |
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072 | 7 |
_aCOM072000 _2bisacsh |
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082 | 0 | 4 |
_a003.3 _223 |
100 | 1 |
_aPassino, Kevin M. _eauthor. |
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245 | 1 | 0 |
_aBiomimicry for Optimization, Control, and Automation _h[electronic resource] / _cby Kevin M. Passino. |
264 | 1 |
_aLondon : _bSpringer London, _c2005. |
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300 |
_aXXXI, 926 p. 365 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |