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Neural Networks and Micromechanics (Hardcover, 2010 ed.)
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Neural Networks and Micromechanics (Hardcover, 2010 ed.)
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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.
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