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Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities

Name: Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities
Brand: Springer Nature Switzerland AG
SKU: 9783030039486
Price: 105.45 EUR
Availability: InStock

Case Studies in Micromachining Processes

Автор: Gerardo Beruvides

Език:

Корици: С твърди корици

Издател: Springer Nature Switzerland AG

Наличност: Външен склад

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105.45 € 206.25 лв

This book introduces three key issues: (i) development of a gradient-free method to enable multi-obj...

Информация за книгата

Автор

Gerardo Beruvides

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2019

страници

195

EAN

9783030039486

Enbook ID

20392768

Издател

Springer Nature Switzerland AG

Теглоt

512

Размери

155 x 235 x 18

Пълно описание

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.