| Titre : |
Biomimicry for Optimization, Control, and Automation |
| Type de document : |
texte imprimé |
| Auteurs : |
Kevin M. Passino, Auteur |
| Editeur : |
London : Springer-Verlag |
| Année de publication : |
2005 |
| Importance : |
926 p. |
| Présentation : |
couv. ill. en coul., ill. |
| Format : |
24 cm. |
| ISBN/ISSN/EAN : |
978-1-85233-804-6 |
| Langues : |
Anglais (eng) |
| Catégories : |
AUTOMATISME
|
| Mots-clés : |
Biomimicry Control Neural Optimization Simulation algorithms automation autonom genetic algorithms robot |
| Index. décimale : |
25-07 Théorie de la commande: commandes des processus |
| Résumé : |
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. |
| Note de contenu : |
Contents
Part I: Introduction:
1 Challenges of control and automation (appropriate for non-controls person),
2 scientific foundations of biomimicry.
Part II: Elements of Decision-Making:
4 Neural network substrates for control instincts,
5 rule-based control,
6 planning systems,
7 attentional systems (including stability analysis).
Part III: Learning:
9 Learning and control (off/on-line approximation perspective, heuristic adaptive control),
10 linear least squares methods (batch and recrusive least squares),
11 gradient methods (e.g. Levenberg-Marquardt),
12 adaptive control (optimization perspective, introduction to stable adaptive control).
Part IV: Evolution:
14 The Genetic algorithm,
15 Stochastic and nongradient optimization for design (e.g., pattern search methods, response surface methodology),
16 Evolution and learning: synergistic effects.
Part V: Foraging:
18 Cooperative foraging and search (optimization models, swarm stability),
19 competitive and intelligent foraging (game-theoretic formulations, outlook on future of area).
-Index |
Biomimicry for Optimization, Control, and Automation [texte imprimé] / Kevin M. Passino, Auteur . - London : Springer-Verlag, 2005 . - 926 p. : couv. ill. en coul., ill. ; 24 cm. ISBN : 978-1-85233-804-6 Langues : Anglais ( eng)
| Catégories : |
AUTOMATISME
|
| Mots-clés : |
Biomimicry Control Neural Optimization Simulation algorithms automation autonom genetic algorithms robot |
| Index. décimale : |
25-07 Théorie de la commande: commandes des processus |
| Résumé : |
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. |
| Note de contenu : |
Contents
Part I: Introduction:
1 Challenges of control and automation (appropriate for non-controls person),
2 scientific foundations of biomimicry.
Part II: Elements of Decision-Making:
4 Neural network substrates for control instincts,
5 rule-based control,
6 planning systems,
7 attentional systems (including stability analysis).
Part III: Learning:
9 Learning and control (off/on-line approximation perspective, heuristic adaptive control),
10 linear least squares methods (batch and recrusive least squares),
11 gradient methods (e.g. Levenberg-Marquardt),
12 adaptive control (optimization perspective, introduction to stable adaptive control).
Part IV: Evolution:
14 The Genetic algorithm,
15 Stochastic and nongradient optimization for design (e.g., pattern search methods, response surface methodology),
16 Evolution and learning: synergistic effects.
Part V: Foraging:
18 Cooperative foraging and search (optimization models, swarm stability),
19 competitive and intelligent foraging (game-theoretic formulations, outlook on future of area).
-Index |
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