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710	2		_aSpringerLink (Online service) _9106996
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003			ES-MaUEC
005			20240111050156.0
008			181214s2019 gw a o |||| 0|eng d
020			_a9783030039493
024	7		_a10.1007/978-3-030-03949-3 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ335 _b2019 EB
100	1		_aBeruvides, Gerardo _eautor _9671430
245	1	0	_aArtificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities : _bCase Studies in Micromachining Processes _cby Gerardo Beruvides.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XXIX, 195 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aSpringer Theses Recognizing Outstanding Ph.D. Research _x2190-5053
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Modeling Techniques for Micromachining Processes -- Cross Entropy Multi-Objectve Optimization Algorithm -- Artificial Cognitive Architecture Design and Implementation.
520	3		_aThis book introduces three key issues: (i) development of a gradient-free method to enable multi-objective self-optimization; (ii) development of a reinforcement learning strategy to carry out self-learning and finally, (iii) experimental evaluation and validation in two micromachining processes (i.e., micro-milling and micro-drilling). The computational architecture (modular, network and reconfigurable for real-time monitoring and control) takes into account the analysis of different types of sensors, processing strategies and methodologies for extracting behavior patterns from representative process' signals. The reconfiguration capability and portability of this architecture are supported by two major levels: the cognitive level (core) and the executive level (direct data exchange with the process). At the same time, the architecture includes different operating modes that interact with the process to be monitored and/or controlled. The cognitive level includes three fundamental modes such as modeling, optimization and learning, which are necessary for decision-making (in the form of control signals) and for the real-time experimental characterization of complex processes. In the specific case of the micromachining processes, a series of models based on linear regression, nonlinear regression and artificial intelligence techniques were obtained. On the other hand, the executive level has a constant interaction with the process to be monitored and/or controlled. This level receives the configuration and parameterization from the cognitive level to perform the desired monitoring and control tasks.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783030039486
776	0	8	_iPrinted edition: _z9783030039509
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-03949-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI