Optimization Techniques
eBook - PDF

Optimization Techniques

  1. 398 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Optimization Techniques

About this book

Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.- Provides in-depth treatment of theoretical contributions to optimal learning for neural network systems- Offers a comprehensive treatment of orthogonal transformation techniques for the optimization of neural network systems- Includes illustrative examples and comprehensive treatment of sequential constructive techniques for optimization of neural network systems- Presents a uniquely comprehensive treatment of the highly effective fast back propagation algorithms for the optimization of neural network systems- Treats, in detail, optimization techniques for neural network systems with nonstationary or dynamic inputs- Covers optimization techniques and applications of neural network systems in constraint satisfaction

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Yes, you can access Optimization Techniques by Cornelius T. Leondes in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Front Cover
  2. Optimization Techniques
  3. Copyright Page
  4. Contents
  5. Contributors
  6. Preface
  7. Chapter 1. Optimal Learning in Artificial Neural Networks: A Theoretical View
  8. Chapter 2. Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems
  9. Chapter 3. Sequential Constructive Techniques
  10. Chapter 4. Fast Backpropagation Training Using Optimal Learning Rate and Momentum
  11. Chapter 5. Learning of Nonstationary Processes
  12. Chapter 6. Constraint Satisfaction Problems
  13. Chapter 7. Dominant Neuron Techniques
  14. Chapter 8. CMAC-Based Techniques for Adaptive Learning Control
  15. Chapter 9. Information Dynamics and Neural Techniques for Data Analysis
  16. Chapter 10. Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems
  17. Index
  18. Erratum