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Bio-inspired algorithms for engineering /

"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:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Alanis, Alma Y.,  |e author. 
245 1 0 |a Bio-inspired algorithms for engineering /  |c Alma Y. Alanis, Nancy Arana-Daniel, Carlos López-Franco. 
250 |a First edition. 
264 1 |a Oxford, United Kingdom :  |b Butterworth-Heinemann, an imprint of Elsevier,  |c [2018] 
264 4 |c ©2018 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 |a Online resource; title from PDF title page (EBSCO, viewed February 13, 2018). 
504 |a Includes bibliographical references and index. 
520 |a "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-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control. Presents real-time implementation and simulation results for all the proposed schemes. Offers a comparative analysis and rigorous analysis of the convergence of proposed algorithms. Provides a guide for implementing each application at the end of each chapterIncludes illustrations, tables and figures that facilitate the reader's comprehension of the proposed schemes and applications"--  |c Provided by publisher 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Computer algorithms. 
650 0 |a Natural computation  |x Industrial applications. 
650 0 |a Evolutionary computation  |x Industrial applications. 
650 0 |a Natural computation  |x Scientific applications. 
650 0 |a Evolutionary computation  |x Scientific applications. 
650 2 |a Algorithms 
650 6 |a Algorithmes. 
650 6 |a Calcul naturel  |x Applications industrielles. 
650 6 |a Réseaux neuronaux à structure évolutive  |x Applications industrielles. 
650 6 |a Calcul naturel  |x Applications scientifiques. 
650 6 |a Réseaux neuronaux à structure évolutive  |x Applications scientifiques. 
650 7 |a algorithms.  |2 aat 
650 7 |a COMPUTERS  |x Programming  |x Algorithms.  |2 bisacsh 
650 7 |a Computer algorithms  |2 fast 
700 1 |a Arana-Daniel, Nancy,  |e author. 
700 1 |a Lopez-Franco, Carlos,  |e author. 
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