Neural Networks and Micromechanics

  • Ernst Kussul
  • Tatiana Baidyk
  • Donald C. Wunsch

Table of contents

  1. Front Matter
    Pages i-x
  2. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 1-5
  3. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 7-25
  4. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 27-46
  5. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 47-73
  6. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 75-104
  7. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 105-129
  8. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 131-140
  9. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 141-194
  10. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 195-203
  11. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 205-209
  12. Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II
    Pages 211-221
  13. Back Matter
    Pages 1-1

About this book

Introduction

Micromechanical manufacturing based on microequipment creates new possibi- ties in goods production. If microequipment sizes are comparable to the sizes of the microdevices to be produced, it is possible to decrease the cost of production drastically. The main components of the production cost - material, energy, space consumption, equipment, and maintenance - decrease with the scaling down of equipment sizes. To obtain really inexpensive production, labor costs must be reduced to almost zero. For this purpose, fully automated microfactories will be developed. To create fully automated microfactories, we propose using arti?cial neural networks having different structures. The simplest perceptron-like neural network can be used at the lowest levels of microfactory control systems. Adaptive Critic Design, based on neural network models of the microfactory objects, can be used for manufacturing process optimization, while associative-projective neural n- works and networks like ART could be used for the highest levels of control systems. We have examined the performance of different neural networks in traditional image recognition tasks and in problems that appear in micromechanical manufacturing. We and our colleagues also have developed an approach to mic- equipment creation in the form of sequential generations. Each subsequent gene- tion must be of a smaller size than the previous ones and must be made by previous generations. Prototypes of ?rst-generation microequipment have been developed and assessed.

Keywords

Image recognition Learning Microassembly, micromachining Micromechanics Neural classifiers Neural network algorithms Neural networks Neurocomputing Texture recognition algorithms artificial intelligence intelligence

Authors and affiliations

  • Ernst Kussul
    • 1
  • Tatiana Baidyk
    • 2
  • Donald C. Wunsch
    • 3
  1. 1.Centro de Ciencias Aplicadas yUniversidad Nacional Autónoma de MéxicoMéxicoMexico
  2. 2.Centro de Ciencias Aplicadas yUniversidad Nacional Autónoma de MéxicoMéxicoMexico
  3. 3.TechnologyMissouri University of Science &RollaU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-02535-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-02534-1
  • Online ISBN 978-3-642-02535-8
  • About this book