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|a 9781846280696
|9 978-1-84628-069-6
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|a 10.1007/b138169
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|a Passino, Kevin M.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Biomimicry for Optimization, Control, and Automation
|h [electronic resource] /
|c by Kevin M. Passino.
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|a 1st ed. 2005.
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|a London :
|b Springer London :
|b Imprint: Springer,
|c 2005.
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|a XXXI, 926 p. 365 illus.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|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.
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|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.
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|a Computer simulation.
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|a Mathematical optimization.
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|a Control engineering.
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|a Robotics.
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|a Automation.
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|a Telecommunication.
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|a Computer Modelling.
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|a Optimization.
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|a Control, Robotics, Automation.
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|a Communications Engineering, Networks.
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9781848007598
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|i Printed edition:
|z 9781852338046
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|u https://doi.uam.elogim.com/10.1007/b138169
|z Texto Completo
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|a ZDB-2-SCS
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|a ZDB-2-SXCS
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|a Computer Science (SpringerNature-11645)
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|a Computer Science (R0) (SpringerNature-43710)
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