| 书目名称 | Neural Advances in Processing Nonlinear Dynamic Signals | | 编辑 | Anna Esposito,Marcos Faundez-Zanuy,Eros Pasero | | 视频video | | | 概述 | Presents neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generations, fi | | 丛书名称 | Smart Innovation, Systems and Technologies | | 图书封面 |  | | 描述 | .This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors, and perturbations in the automatic control of industrial production processes. It also discusses the drastic changes in financial, economic, and work processes that are currently being experienced by the computational and engineering sciences community..Addresses key aspects, such as the integration of neural algorithms and procedures for the recognition, the analysis and detection of dynamic complex structures and the implementation of systems for discovering patterns in data, the book highlights the commonalities between computational intelligence (CI) and information and communications technologies (ICT) to promote transversal skills and sophisticated processing techniques..This book is a valuable resource for.a. The academic research community.b. The ICT market.c. PhD students and early stage researchers.d. Companies, research institutes.e. Representatives from industry and standardization bodies. | | 出版日期 | Book 2019 | | 关键词 | Computational Intelligence; Financial and Industrial Process; Speech Enhancement; Machine Learning Meth | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-95098-3 | | isbn_softcover | 978-3-030-06977-3 | | isbn_ebook | 978-3-319-95098-3Series ISSN 2190-3018 Series E-ISSN 2190-3026 | | issn_series | 2190-3018 | | copyright | Springer International Publishing AG, part of Springer Nature 2019 |
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发表于 2025-3-21 16:29:16
