Bio-inspired Algorithms for Engineering, 1st Edition

Bio-inspired Algorithms for Engineering, 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9780128137888,0128137886,9780128137895, 0128137894

Product detail:

• ISBN 10: 0128137894
• ISBN 13: 9780128137895
• Author: Nancy Arana-Daniel, Carlos Lopez-Franco, Alma Y Alanis

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:

1. Chapter One: Bio-inspired Algorithms
2. Chapter Two: Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron
3. Chapter Three: Reconstruction of 3D Surfaces Using RBF Adjusted with PSO
4. Chapter Four: Soft Computing Applications in Robot Vision
5. Chapter Five: Soft Computing Applications in Mobile Robotics
6. Chapter Six: Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems
7. Chapter Seven: Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System

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