Book description
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 chapter
- Includes illustrations, tables and figures that facilitate the reader’s comprehension of the proposed schemes and applications
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- Acknowledgments
- Chapter One: Bio-inspired Algorithms
-
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 algorithm
- 2.5. 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
- Chapter Four: Soft Computing Applications in Robot Vision
- Chapter Five: Soft Computing Applications in Mobile Robotics
- Chapter Six: Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems
-
Chapter Seven: Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System
- Abstract
- 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
Product information
- Title: Bio-inspired Algorithms for Engineering
- Author(s):
- Release date: February 2018
- Publisher(s): Butterworth-Heinemann
- ISBN: 9780128137895
You might also like
book
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner …
book
Swarm Intelligence and Bio-Inspired Computation
Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms …
book
Statistical Learning for Big Dependent Data
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical …
book
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements …