| Título : |
Biomimicry for optimization, control, and automation |
| Tipo de documento: |
texto impreso |
| Autores: |
Kevin M. Passino, Autor |
| Editorial: |
Berlin [Alemania] : Springer |
| Fecha de publicación: |
2004 |
| Número de páginas: |
960 p. |
| Il.: |
il., ... en blanco y negro |
| Dimensiones: |
24 cm. |
| ISBN/ISSN/DL: |
978-1-85233-804-6 |
| Precio: |
299000 |
| Idioma : |
Inglés (eng) |
| Etiquetas: |
Ingeniería Mecatrónica Optimizacion matematica Redes Robots-sistemas de control |
| Resumen: |
There are many highly effective optimization, feedback control, and automation systems embedded in living organisms and nature. Evolution persistently seeks optimal robust designs for biological feedback control systems and decision making processes.
From this comprehensive text you will gain knowledge of how mimicry of such biological processes can be used to solve optimization, control, and automation problems encountered in the construction of high technology systems. Mathematical stability analysis is treated for a number of cases, from attentional systems to social foraging swarm cohesion properties. Bio-inspired optimization and control methods are compared to conventional techniques with an objective to provide a balanced viewpoint.
* A companion web site, continually updated by the author, will provide you with futher examples and design problems, solution hints, lecture slides, a running lab and ongoing self-study problems and resources.
* Matlab code is provided to solve a number of key problems.
* Focus lies on verifying correct operation of technologies via a process of mathematical modelling and analysis complimented by computer simulations.
* Written from an engineering perspective, methods are applied to extensive real-world applications, from ship steering to cooperative control of a group of autonomous robots.
Aimed primarily at graduate courses and research, much of the material has been successfully used for undergraduate courses. This dynamic textbook sends an injection of new ideas into engineering technology and the academic community.
Hay muchos sistemas altamente efectivos de optimización, control de retroalimentación y automatización integrados en los organismos vivos y la naturaleza. La evolución busca persistentemente diseños robustos óptimos para los sistemas de control de retroalimentación biológica y los procesos de toma de decisiones.
A partir de este completo texto, obtendrá conocimientos sobre cómo se puede utilizar la imitación de tales procesos biológicos para resolver los problemas de optimización, control y automatización que se encuentran en la construcción de sistemas de alta tecnología. El análisis de estabilidad matemática se trata para una serie de casos, desde sistemas atencionales hasta propiedades de cohesión de enjambres de forrajeo social. Los métodos de control y optimización bioinspirados se comparan con las técnicas convencionales con el objetivo de proporcionar un punto de vista equilibrado.
* Un sitio web complementario, continuamente actualizado por el autor, le proporcionará más ejemplos y problemas de diseño, sugerencias de soluciones, diapositivas de conferencias, un laboratorio en funcionamiento y problemas y recursos continuos de autoaprendizaje.
* El código de Matlab se proporciona para resolver una serie de problemas clave.
* El enfoque radica en verificar el funcionamiento correcto de las tecnologías a través de un proceso de modelado y análisis matemático complementado con simulaciones por computadora.
* Escrito desde una perspectiva de ingeniería, los métodos se aplican a aplicaciones extensas del mundo real, desde el gobierno de barcos hasta el control cooperativo de un grupo de robots autónomos.
Dirigido principalmente a cursos de posgrado e investigación, gran parte del material se ha utilizado con éxito para cursos de pregrado. Este libro de texto dinámico envía una inyección de nuevas ideas a la tecnología de la ingeniería y a la comunidad académica.
|
| Nota de contenido: |
Contents
I. INTRODUCTION.
1. Challenges in computer control and automation.
1.1. The Role of Traditional Feedback Control Systems in Automation.
1.2. Design Objectives for Control Systems.
1.3. Control System Design Methodology.
1.4. Complex Hierarchical Control Systems for Automation.
1.5. Design Objectives for Automation.
1.6. Software Engineering for Complex Control Systems.
1.7. Implementing Complex Control Systems.
1.8. Hybrid System Theory and Analysis.
1.9. Exercises.
2. Scientific Foundations for Biomimicry.
2.1. Control Systems in Biology.
2.2. Nervous Systems.
2.3. Organisms.
2.4. Groups of Organisms.
2.5. Evolution.
2.6. A Control Engineering Viewpoint.
2.7. Exercises.
3. For Further Study.
II. ELEMENTS OF DECISION MAKING.
4. Neural Network Substrates for Control Instincts.
4.1. Biological Neural Networks and Their Role in Control.
4.2. Multilayer Perceptrons.
4.3. Design Example: Multilayer Perceptron for Tanker Ship Steering.
4.4. Radial Basis Function Neural Networks.
4.5. Design Example: Radial Basis Function Neural Network for Ship Steering.
4.6. Stability Analysis.
4.7. Hierarchical Neural Networks.
4.8. Exercises and Design Problems.
5. Rule-Based Control.
5.1. Fuzzy Control.
5.2. General Fuzzy Systems.
5.3. Design Example: Fuzzy Control for Tanker Ship Steering.
5.4. Stability Analysis.
5.5. Expert Control.
5.6. Hierarchical Rule-Based Control Systems.
5.7. Exercises and Design Problems.
6. Planning Systems.
6.1. Psychology of Planning.
6.2. Design Example: Vehicle Guidance.
6.3. Planning Strategy Design.
6.4. Design Example: Planning for a Process Control Problem.
6.5. Exercises and Design Problems.
7. Attentional Systems.
7.1. Neuroscience and Psychology of Attention.
7.2. Dynamics of Attention: Search and Optimization Perspective.
7.3. Attentional Strategies for Multiple Predators and Prey.
7.4. Design Example: Attentional Strategies.
7.5. Stability Analysis of Attentional Strategies.
7.6. Attentional Systems in Control and Automation.
7.7. Exercises and Design Problems.
8. For Further Study.
III. LEARNING.
9. Learning and Control.
9.1. Psychology and Neuroscience of Learning.
9.2. Function Approximation as Learning.
9.3. Approximator Structures as Substrates for Learning.
9.4. Biomimicry for Heuristic Adaptive Control.
9.5. Exercises and Design Problems.
10. Linear Least Squares Methods.
10.1. Batch Least Squares.
10.2. Example: Offline Tuning of Approximators.
10.3. Design Example: Rule Synthesis Using Operator Data.
10.4. Recursive Least Squares.
10.5. Example: Online Tuning of Approximators.
10.6. Exercises and Design Problems.
11. Gradient Methods.
11.1. The Steepest Descent Method.
11.2. Levenberg-Marquardt and Conjugate Gradient Methods.
11.3. Matlab for Training Neural Networks.
11.4. Example: Levenberg-Marquardt Training of a Fuzzy System.
11.5. Example: Online Steepest Descent Training of a Neural Network.
11.6. Clustering for Classifiers and Approximators.
11.7. Neural or Fuzzy: Which is Better? Bad Question!
11.8. Exercises and Design Problems.
12. Adaptive Control.
12.1. Strategies for Adaptive Control.
12.2. Classes of Nonlinear Discrete-Time Systems.
12.3. Indirect Adaptive Neural/Fuzzy Control.
12.4. Design Example: Indirect Neural Control for a Process Control Problem.
12.5. Direct Adaptive Neural/Fuzzy Control.
12.6. Design Example: Direct Neural Control for a Process Control Problem.
12.7. Stable Adaptive Fuzzy/Neural Control.
12.8. Discussion: Tuning Structure and Nonlinear in the Parameter Approximators.
12.9. Exercises and Design Problems.
13. For Further Study.
IV. EVOLUTION.
14. The Genetic Algorithm.
14.1. Biological Evolution.
14.2. Representing the Population of Individuals.
14.3. Genetic Operations.
14.4. Programming the Genetic Algorithm.
14.5. Example: Solving an Optimization Problem.
14.6. Approximations to Reduce Algorithm Complexity.
14.7. Exercises and Design Problems.
15. Stochastic and Nongradient Optimization for Design.
15.1. Design of Robust Organisms and Systems.
15.2. Response Surface Methodology for Design.
15.3. Nongradient Optimization.
15.4. SPSA for Decision-Making System Design: Examples.
15.5. Parallel, Interleaved, and Hierarchical Nongradient Methods.
15.6. Set-Based Stochastic Optimization for Design.
15.7. Discussion: Evolutionary Control System Design.
15.8. Exercises and Design Problems.
16. Evolution and Learning: Synergistic Effects.
16.1. Relevant Theories of Biological Evolution.
16.2. Robust Approximator Size Design
16.3. Instinct-Learning Balance in an Uncertain Environment.
16.4. Discussion: Instinct-Learning Balance for Adaptive Control.
16.5. Genetic Adaptive Control.
16.6. Exercises and Design Problems.
17. For Further Study.
V. FORAGING.
18. Cooperative Foraging and Search.
18.1. Foraging Theory.
18.2. Bacterial Foraging: E. coli.
18.3. E. coli Bacterial Swarm Foraging for Optimization.
18.4. Stable Social Foraging Swarms.
18.5. Design Example: Robot Swarms.
18.6. Exercises and Design Problems.
19. Competitive and Intelligent Foraging.
19.1. Competition and Fighting in Nature.
19.2. Introduction to Game Theory.
19.3. Design Example: Static Foraging Games.
19.4. Dynamic Games.
19.5. Example: Dynamic Foraging Games.
19.6. Challenge Problems: Intelligent Social Foraging.
19.7. Exercises and Design Problems.
20. For Further Study.
· BIBLIOGRAPHY
· INDEX
|
Biomimicry for optimization, control, and automation [texto impreso] / Kevin M. Passino, Autor . - Berlin [Alemania] : Springer, 2004 . - 960 p. : il., ... en blanco y negro ; 24 cm. ISBN : 978-1-85233-804-6 : 299000 Idioma : Inglés ( eng)
| Etiquetas: |
Ingeniería Mecatrónica Optimizacion matematica Redes Robots-sistemas de control |
| Resumen: |
There are many highly effective optimization, feedback control, and automation systems embedded in living organisms and nature. Evolution persistently seeks optimal robust designs for biological feedback control systems and decision making processes.
From this comprehensive text you will gain knowledge of how mimicry of such biological processes can be used to solve optimization, control, and automation problems encountered in the construction of high technology systems. Mathematical stability analysis is treated for a number of cases, from attentional systems to social foraging swarm cohesion properties. Bio-inspired optimization and control methods are compared to conventional techniques with an objective to provide a balanced viewpoint.
* A companion web site, continually updated by the author, will provide you with futher examples and design problems, solution hints, lecture slides, a running lab and ongoing self-study problems and resources.
* Matlab code is provided to solve a number of key problems.
* Focus lies on verifying correct operation of technologies via a process of mathematical modelling and analysis complimented by computer simulations.
* Written from an engineering perspective, methods are applied to extensive real-world applications, from ship steering to cooperative control of a group of autonomous robots.
Aimed primarily at graduate courses and research, much of the material has been successfully used for undergraduate courses. This dynamic textbook sends an injection of new ideas into engineering technology and the academic community.
Hay muchos sistemas altamente efectivos de optimización, control de retroalimentación y automatización integrados en los organismos vivos y la naturaleza. La evolución busca persistentemente diseños robustos óptimos para los sistemas de control de retroalimentación biológica y los procesos de toma de decisiones.
A partir de este completo texto, obtendrá conocimientos sobre cómo se puede utilizar la imitación de tales procesos biológicos para resolver los problemas de optimización, control y automatización que se encuentran en la construcción de sistemas de alta tecnología. El análisis de estabilidad matemática se trata para una serie de casos, desde sistemas atencionales hasta propiedades de cohesión de enjambres de forrajeo social. Los métodos de control y optimización bioinspirados se comparan con las técnicas convencionales con el objetivo de proporcionar un punto de vista equilibrado.
* Un sitio web complementario, continuamente actualizado por el autor, le proporcionará más ejemplos y problemas de diseño, sugerencias de soluciones, diapositivas de conferencias, un laboratorio en funcionamiento y problemas y recursos continuos de autoaprendizaje.
* El código de Matlab se proporciona para resolver una serie de problemas clave.
* El enfoque radica en verificar el funcionamiento correcto de las tecnologías a través de un proceso de modelado y análisis matemático complementado con simulaciones por computadora.
* Escrito desde una perspectiva de ingeniería, los métodos se aplican a aplicaciones extensas del mundo real, desde el gobierno de barcos hasta el control cooperativo de un grupo de robots autónomos.
Dirigido principalmente a cursos de posgrado e investigación, gran parte del material se ha utilizado con éxito para cursos de pregrado. Este libro de texto dinámico envía una inyección de nuevas ideas a la tecnología de la ingeniería y a la comunidad académica.
|
| Nota de contenido: |
Contents
I. INTRODUCTION.
1. Challenges in computer control and automation.
1.1. The Role of Traditional Feedback Control Systems in Automation.
1.2. Design Objectives for Control Systems.
1.3. Control System Design Methodology.
1.4. Complex Hierarchical Control Systems for Automation.
1.5. Design Objectives for Automation.
1.6. Software Engineering for Complex Control Systems.
1.7. Implementing Complex Control Systems.
1.8. Hybrid System Theory and Analysis.
1.9. Exercises.
2. Scientific Foundations for Biomimicry.
2.1. Control Systems in Biology.
2.2. Nervous Systems.
2.3. Organisms.
2.4. Groups of Organisms.
2.5. Evolution.
2.6. A Control Engineering Viewpoint.
2.7. Exercises.
3. For Further Study.
II. ELEMENTS OF DECISION MAKING.
4. Neural Network Substrates for Control Instincts.
4.1. Biological Neural Networks and Their Role in Control.
4.2. Multilayer Perceptrons.
4.3. Design Example: Multilayer Perceptron for Tanker Ship Steering.
4.4. Radial Basis Function Neural Networks.
4.5. Design Example: Radial Basis Function Neural Network for Ship Steering.
4.6. Stability Analysis.
4.7. Hierarchical Neural Networks.
4.8. Exercises and Design Problems.
5. Rule-Based Control.
5.1. Fuzzy Control.
5.2. General Fuzzy Systems.
5.3. Design Example: Fuzzy Control for Tanker Ship Steering.
5.4. Stability Analysis.
5.5. Expert Control.
5.6. Hierarchical Rule-Based Control Systems.
5.7. Exercises and Design Problems.
6. Planning Systems.
6.1. Psychology of Planning.
6.2. Design Example: Vehicle Guidance.
6.3. Planning Strategy Design.
6.4. Design Example: Planning for a Process Control Problem.
6.5. Exercises and Design Problems.
7. Attentional Systems.
7.1. Neuroscience and Psychology of Attention.
7.2. Dynamics of Attention: Search and Optimization Perspective.
7.3. Attentional Strategies for Multiple Predators and Prey.
7.4. Design Example: Attentional Strategies.
7.5. Stability Analysis of Attentional Strategies.
7.6. Attentional Systems in Control and Automation.
7.7. Exercises and Design Problems.
8. For Further Study.
III. LEARNING.
9. Learning and Control.
9.1. Psychology and Neuroscience of Learning.
9.2. Function Approximation as Learning.
9.3. Approximator Structures as Substrates for Learning.
9.4. Biomimicry for Heuristic Adaptive Control.
9.5. Exercises and Design Problems.
10. Linear Least Squares Methods.
10.1. Batch Least Squares.
10.2. Example: Offline Tuning of Approximators.
10.3. Design Example: Rule Synthesis Using Operator Data.
10.4. Recursive Least Squares.
10.5. Example: Online Tuning of Approximators.
10.6. Exercises and Design Problems.
11. Gradient Methods.
11.1. The Steepest Descent Method.
11.2. Levenberg-Marquardt and Conjugate Gradient Methods.
11.3. Matlab for Training Neural Networks.
11.4. Example: Levenberg-Marquardt Training of a Fuzzy System.
11.5. Example: Online Steepest Descent Training of a Neural Network.
11.6. Clustering for Classifiers and Approximators.
11.7. Neural or Fuzzy: Which is Better? Bad Question!
11.8. Exercises and Design Problems.
12. Adaptive Control.
12.1. Strategies for Adaptive Control.
12.2. Classes of Nonlinear Discrete-Time Systems.
12.3. Indirect Adaptive Neural/Fuzzy Control.
12.4. Design Example: Indirect Neural Control for a Process Control Problem.
12.5. Direct Adaptive Neural/Fuzzy Control.
12.6. Design Example: Direct Neural Control for a Process Control Problem.
12.7. Stable Adaptive Fuzzy/Neural Control.
12.8. Discussion: Tuning Structure and Nonlinear in the Parameter Approximators.
12.9. Exercises and Design Problems.
13. For Further Study.
IV. EVOLUTION.
14. The Genetic Algorithm.
14.1. Biological Evolution.
14.2. Representing the Population of Individuals.
14.3. Genetic Operations.
14.4. Programming the Genetic Algorithm.
14.5. Example: Solving an Optimization Problem.
14.6. Approximations to Reduce Algorithm Complexity.
14.7. Exercises and Design Problems.
15. Stochastic and Nongradient Optimization for Design.
15.1. Design of Robust Organisms and Systems.
15.2. Response Surface Methodology for Design.
15.3. Nongradient Optimization.
15.4. SPSA for Decision-Making System Design: Examples.
15.5. Parallel, Interleaved, and Hierarchical Nongradient Methods.
15.6. Set-Based Stochastic Optimization for Design.
15.7. Discussion: Evolutionary Control System Design.
15.8. Exercises and Design Problems.
16. Evolution and Learning: Synergistic Effects.
16.1. Relevant Theories of Biological Evolution.
16.2. Robust Approximator Size Design
16.3. Instinct-Learning Balance in an Uncertain Environment.
16.4. Discussion: Instinct-Learning Balance for Adaptive Control.
16.5. Genetic Adaptive Control.
16.6. Exercises and Design Problems.
17. For Further Study.
V. FORAGING.
18. Cooperative Foraging and Search.
18.1. Foraging Theory.
18.2. Bacterial Foraging: E. coli.
18.3. E. coli Bacterial Swarm Foraging for Optimization.
18.4. Stable Social Foraging Swarms.
18.5. Design Example: Robot Swarms.
18.6. Exercises and Design Problems.
19. Competitive and Intelligent Foraging.
19.1. Competition and Fighting in Nature.
19.2. Introduction to Game Theory.
19.3. Design Example: Static Foraging Games.
19.4. Dynamic Games.
19.5. Example: Dynamic Foraging Games.
19.6. Challenge Problems: Intelligent Social Foraging.
19.7. Exercises and Design Problems.
20. For Further Study.
· BIBLIOGRAPHY
· INDEX
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