國立高雄大學圖資館 |

Language: English

Back

Artificial cognitive architecture wi...

Beruvides, Gerardo.

Artificial cognitive architecture with self-learning and self-optimization capabilitiescase studies in micromachining processes /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial cognitive architecture with self-learning and self-optimization capabilitiesby Gerardo Beruvides.
Reminder of title:	case studies in micromachining processes /
Author:	Beruvides, Gerardo.
Published:	Cham :Springer International Publishing :2019.
Description:	xxix, 195 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-030-03949-3
ISBN:	9783030039493$q(electronic bk.)

Artificial cognitive architecture with self-learning and self-optimization capabilitiescase studies in micromachining processes /
Beruvides, Gerardo.

Artificial cognitive architecture with self-learning and self-optimization capabilitiescase studies in micromachining processes /[electronic resource] :by Gerardo Beruvides. - Cham :Springer International Publishing :2019. - xxix, 195 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Modeling Techniques for Micromachining Processes -- Cross Entropy Multi-Objectve Optimization Algorithm -- Artificial Cognitive Architecture Design and Implementation.

This 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.

ISBN: 9783030039493$q(electronic bk.)

Standard No.: 10.1007/978-3-030-03949-3doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .B478 2019

Dewey Class. No.: 006.3

Artificial cognitive architecture with self-learning and self-optimization capabilitiescase studies in micromachining processes /
LDR:02909nmm a2200337 a 4500 001 555688
003 DE-He213
005 20190705151551.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783030039493$q(electronic bk.)
020 $a 9783030039486$q(paper)
024 7 $a 10.1007/978-3-030-03949-3 $2 doi
035 $a 978-3-030-03949-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q335 $b .B478 2019
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q335 $b .B552 2019
100 1 $a Beruvides, Gerardo. $3 837895
245 1 0 $a Artificial cognitive architecture with self-learning and self-optimization capabilities $h [electronic resource] : $b case studies in micromachining processes / $c by Gerardo Beruvides.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xxix, 195 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Modeling Techniques for Micromachining Processes -- Cross Entropy Multi-Objectve Optimization Algorithm -- Artificial Cognitive Architecture Design and Implementation.
520 $a This 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.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Cognitive science. $3 190026
650 0 $a Machine learning. $3 188639
650 0 $a Mathematical optimization. $3 183292
650 0 $a Micromachining. $3 200384
650 1 4 $a Control and Systems Theory. $3 825946
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Manufacturing, Machines, Tools, Processes. $3 833130
650 2 4 $a Artificial Intelligence. $3 212515
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer theses. $3 557607
856 4 0 $u https://doi.org/10.1007/978-3-030-03949-3
950 $a Intelligent Technologies and Robotics (Springer-42732)