Neural Networks and Micromechanics

1. Front Matter

   Pages i-x

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2. Introduction

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 1-5

3. Classical Neural Networks

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 7-25

4. Neural Classifiers

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 27-46

5. Permutation Coding Technique for Image Recognition System

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 47-73

6. Associative-Projective Neural Networks (APNNs)

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 75-104

7. Recognition of Textures, Object Shapes, and Handwritten Words

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 105-129

8. Hardware for Neural Networks

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 131-140

9. Micromechanics

   • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

   Pages 141-194

10. Applications of Neural Networks in Micromechanics

    • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

    Pages 195-203

11. Texture Recognition in Micromechanics

    • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

    Pages 205-209

12. Adaptive Algorithms Based on Technical Vision

    • Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch II

    Pages 211-221

13. Back Matter

    Pages 1-1

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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.

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

• Centro de Ciencias Aplicadas y, Universidad Nacional Autónoma de México, México, Mexico

  Ernst Kussul, Tatiana Baidyk

• Technology, Missouri University of Science &, Rolla, U.S.A.

  Donald C. Wunsch

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