Online resource; title from PDF title page (EBSCO, viewed February 13, 2018).

Includes bibliographical references and index.

"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"-- Provided by publisher.

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

Elsevier ScienceDirect All Books