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Titlebook: Computational Intelligence; 11th International J Juan Julián Merelo,Jonathan Garibaldi,Kurosh Madan Conference proceedings 2021 Springer Na

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發(fā)表于 2025-3-21 17:26:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computational Intelligence
副標(biāo)題11th International J
編輯Juan Julián Merelo,Jonathan Garibaldi,Kurosh Madan
視頻videohttp://file.papertrans.cn/233/232359/232359.mp4
概述Highlights recent research on Computational Intelligence.Gathers revised papers presented at the 11th International Joint Conference on Computational Intelligence (IJCCI 2019), held in Vienna, Austria
叢書(shū)名稱Studies in Computational Intelligence
圖書(shū)封面Titlebook: Computational Intelligence; 11th International J Juan Julián Merelo,Jonathan Garibaldi,Kurosh Madan Conference proceedings 2021 Springer Na
描述This present book includes a set of selected revised and extended versions of the best papers presented at the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) – held in Vienna, Austria, from 17 to 19 September 2019. The authors focus on three outstanding fields of Computational Intelligence through the selected panel, namely Evolutionary Computation, Fuzzy Computation and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and, on the other hand, to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current filed of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represents a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques invarious fields of applications.
出版日期Conference proceedings 2021
關(guān)鍵詞Computational Intelligence; Evolutionary Computation; Fuzzy Computation; Neural Computation; IJCCI Paper
版次1
doihttps://doi.org/10.1007/978-3-030-70594-7
isbn_softcover978-3-030-70596-1
isbn_ebook978-3-030-70594-7Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

書(shū)目名稱Computational Intelligence影響因子(影響力)




書(shū)目名稱Computational Intelligence影響因子(影響力)學(xué)科排名




書(shū)目名稱Computational Intelligence網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computational Intelligence網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computational Intelligence被引頻次




書(shū)目名稱Computational Intelligence被引頻次學(xué)科排名




書(shū)目名稱Computational Intelligence年度引用




書(shū)目名稱Computational Intelligence年度引用學(xué)科排名




書(shū)目名稱Computational Intelligence讀者反饋




書(shū)目名稱Computational Intelligence讀者反饋學(xué)科排名




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Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence on a financial market. Market timing strategies can be built by using a collection of components or functions that process market context and return a recommendation on the course of action to take. In this chapter, we revisit the work presented in [.] on the application of Genetic Algorithms (GA)
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Hybrid Strategy Coupling EGO and CMA-ES for Structural Topology Optimization in Statics and Crashworlications. However, many TO optimization techniques are still questionable when applied to crashworthiness optimization problems due to their complexity and lack of gradient information. The aim of this work is to describe the Hybrid Kriging-assisted Level Set Method (HKG-LSM) and test its performan
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Handling Complexity in Some Typical Problems of Distributed Systems by Using Self-organizing Principnents, heterogeneity, problem size and dynamic changes of the environment. These challenges are especially emphasized in distributed software systems. To cope with unforeseen dynamics in the environment and vast number of unpredictable dependencies on participating components, employing of self-orga
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Fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interactionue of the human-robot navigation is the computation of the intersection point in the presence of noisy measurements or fuzzy information. For Gaussian distributions of positions/orientations (inputs) of robot and human agent and their parameters the corresponding parameters at the intersections (out
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