000			03777nam a22005415i 4500
001			u373451
003			SIRSI
005			20160812084146.0
007			cr nn 008mamaa
008			100301s2010 gw | s |||| 0|eng d
020			_a9783642025358 _9978-3-642-02535-8
040			_cMX-MeUAM
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
082	0	4	_a006.3 _223
100	1		_aKussul, Ernst. _eauthor.
245	1	0	_aNeural Networks and Micromechanics _h[recurso electrónico] / _cby Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aX, 221 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aClassical Neural Networks -- Neural Classifiers -- Permutation Coding Technique for Image Recognition System -- Associative-Projective Neural Networks (APNNs) -- Recognition of Textures, Object Shapes, and Handwritten Words -- Hardware for Neural Networks -- Micromechanics -- Applications of Neural Networks in Micromechanics -- Texture Recognition in Micromechanics -- Adaptive Algorithms Based on Technical Vision.
520			_aMicromechanical 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.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650		0	_aComputer vision.
650		0	_aOptical pattern recognition.
650		0	_aMachinery.
650		0	_aElectronics.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aManufacturing, Machines, Tools.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aPattern Recognition.
650	2	4	_aControl, Robotics, Mechatronics.
650	2	4	_aElectronics and Microelectronics, Instrumentation.
700	1		_aBaidyk, Tatiana. _eauthor.
700	1		_aWunsch, Donald C. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642025341
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-02535-8
596			_a19
942			_cLIBRO_ELEC
999			_c201331 _d201331