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Books > Computing & IT > Applications of computing > Artificial intelligence
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Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities - Case Studies in Micromachining Processes (Hardcover, 1st ed. 2019)
Loot Price: R2,800
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Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities - Case Studies in Micromachining Processes (Hardcover, 1st ed. 2019)
Series: Springer Theses
Expected to ship within 10 - 15 working days
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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.
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