Unified Computational Intelligence For Complex Systems
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Unified Computational Intelligence for Complex Systems
Author: John Seiffertt
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-07-15
Computational intelligence encompasses a wide variety of techniques that allow computation to learn, to adapt, and to seek. That is, they may be designed to learn information without explicit programming regarding the nature of the content to be retained, they may be imbued with the functionality to adapt to maintain their course within a complex and unpredictably changing environment, and they may help us seek out truths about our own dynamics and lives through their inclusion in complex system modeling. These capabilities place our ability to compute in a category apart from our ability to erect suspension bridges, although both are products of technological advancement and reflect an increased understanding of our world. In this book, we show how to unify aspects of learning and adaptation within the computational intelligence framework. While a number of algorithms exist that fall under the umbrella of computational intelligence, with new ones added every year, all of them focus on the capabilities of learning, adapting, and helping us seek. So, the term unified computational intelligence relates not to the individual algorithms but to the underlying goals driving them. This book focuses on the computational intelligence areas of neural networks and dynamic programming, showing how to unify aspects of these areas to create new, more powerful, computational intelligence architectures to apply to new problem domains.
Unified Computational Intelligence for Complex Systems
Computational intelligence encompasses a wide variety of techniques that allow computation to learn, to adapt, and to seek. That is, they may be designed to learn information without explicit programming regarding the nature of the content to be retained, they may be imbued with the functionality to adapt to maintain their course within a complex and unpredictably changing environment, and they may help us seek out truths about our own dynamics and lives through their inclusion in complex system modeling. These capabilities place our ability to compute in a category apart from our ability to erect suspension bridges, although both are products of technological advancement and reflect an increased understanding of our world. In this book, we show how to unify aspects of learning and adaptation within the computational intelligence framework. While a number of algorithms exist that fall under the umbrella of computational intelligence, with new ones added every year, all of them focus on the capabilities of learning, adapting, and helping us seek. So, the term unified computational intelligence relates not to the individual algorithms but to the underlying goals driving them. This book focuses on the computational intelligence areas of neural networks and dynamic programming, showing how to unify aspects of these areas to create new, more powerful, computational intelligence architectures to apply to new problem domains.
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.