Artificial Cognitive Architecture With Self Learning And Self Optimization Capabilities

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

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.

Axionomics

Axionomics presents a comprehensive, recursive framework that unifies axiomatic principles, atomic structures, quantum mechanics, and decentralized knowledge systems into a self-regulating, axiom-driven knowledge and energy economy. By integrating linguistic organization, artificial intelligence (AI), blockchain-backed verification, and decentralized scientific governance, this revolutionary model creates a seamless bridge between foundational principles and applied systems. Operating simultaneously across quantum, atomic, and macroscopic organizational scales, Axionomics leverages recursive feedback loops and self-referential processes to enable continuous adaptation and optimization. This dynamic, self-evolving architecture refines itself in response to new discoveries while preserving core axiomatic integrity, ensuring the stability of knowledge structures even in rapidly advancing scientific fields. By fostering interdisciplinary collaboration, Axionomics reshapes scientific inquiry, computational intelligence, and organizational governance. This system transcends conventional limitations, offering a self-optimizing knowledge ecosystem that harmonizes theory and practice, unlocking new frontiers in innovation, knowledge distribution, and decentralized intelligence networks. As a transformative model, Axionomics redefines how we understand, verify, and apply knowledge, setting the foundation for a future driven by recursive intelligence, axiomatic reasoning, and sustainable progress.

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