| 书目名称 | Unified Computational Intelligence for Complex Systems | | 编辑 | John Seiffertt,Donald C. Wunsch | | 视频video | | | 概述 | First book presenting a computational intelligence architecture capable of learning in unsupervised, supervised, or reinforcement learning modes.The only book covering applications of time scales math | | 丛书名称 | Adaptation, Learning, and Optimization | | 图书封面 |  | | 描述 | 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 foc | | 出版日期 | Book 2010 | | 关键词 | Adaptive Resonance Theory; Agent-Based Computational Social Science; Approximate Dynamic Programming; B | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-03180-9 | | isbn_softcover | 978-3-642-26395-8 | | isbn_ebook | 978-3-642-03180-9Series ISSN 1867-4534 Series E-ISSN 1867-4542 | | issn_series | 1867-4534 | | copyright | Springer-Verlag Berlin Heidelberg 2010 |
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发表于 2025-3-21 17:56:21
