Bio-inspired algorithms for engineering /

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"Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-lif...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Alanis, Alma Y. (Autor), Arana-Daniel, Nancy (Autor), Lopez-Franco, Carlos (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford, United Kingdom : Butterworth-Heinemann, an imprint of Elsevier, [2018]
Edición:First edition.
Temas:
Computer algorithms.
Natural computation > Industrial applications.
Evolutionary computation > Industrial applications.
Natural computation > Scientific applications.
Evolutionary computation > Scientific applications.
Algorithms
Algorithmes.
Calcul naturel > Applications industrielles.
R�eseaux neuronaux �a structure �evolutive > Applications industrielles.
Calcul naturel > Applications scientifiques.
R�eseaux neuronaux �a structure �evolutive > Applications scientifiques.
algorithms.
COMPUTERS > Programming > Algorithms.
Computer algorithms
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro; Title page; Table of Contents; Copyright; Dedication; Preface; Acknowledgments; Chapter One: Bio-inspired Algorithms; Abstract; 1.1. Introduction; 1.2. Particle Swarm Optimization; 1.3. Artificial Bee Colony Algorithm; 1.4. Micro Artificial Bee Colony Algorithm; 1.5. Differential Evolution; 1.6. Bacterial Foraging Optimization Algorithm; References; Chapter Two: Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron; Abstract; 2.1. Introduction; 2.2. Support Vector Machines; 2.3. Evolutionary algorithms.
  • 2.4. The Kernel Adatron algorithm2.5. Kernel Adatron trained with evolutionary algorithms; 2.6. Results using benchmark repository datasets; 2.7. Application to classify electromyographic signals; 2.8. Conclusions; References; Chapter Three: Reconstruction of 3D Surfaces Using RBF Adjusted with PSO; Abstract; 3.1. Introduction; 3.2. Radial basis functions; 3.3. Interpolation of surfaces with RBF and PSO; 3.4. Conclusion; References; Chapter Four: Soft Computing Applications in Robot Vision; Abstract; 4.1. Introduction; 4.2. Image tracking; 4.3. Plane detection; 4.4. Conclusion; References.
  • Chapter Five: Soft Computing Applications in Mobile RoboticsAbstract; 5.1. Introduction to mobile robotics; 5.2. Nonholonomic mobile robot navigation; 5.3. Holonomic mobile robot navigation; 5.4. Conclusion; References; Chapter Six: Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems; Abstract; 6.1. Introduction; 6.2. Particle-swarm-based approach of a real-time discrete neural identifier for Linear Induction Motors; 6.3. Neural model with particle swarm optimization Kalman learning for forecasting in smart grids; 6.4. Conclusions; References.
  • Chapter Seven: Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear SystemAbstract; 7.1. Neural Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems; 7.2. Neural-PSO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems; 7.3. Neural-BFO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems; 7.4. Comparative analysis; 7.5. Conclusions; References; Chapter Eight: Final Remarks; Index.

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