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Neural advances in processing nonlin...

Esposito, Anna.

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Neural advances in processing nonlinear dynamic signals

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural advances in processing nonlinear dynamic signals/ edited by Anna Esposito ... [et al.].
其他作者:	Esposito, Anna.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xii, 318 p. :ill., digital ;24 cm.
內容註:	Processing Nonlinearities -- Temporal Artifacts from Edge Accumulation in Social Interaction -- Data Mining by Evolving Agents for Clusters Discovery and Metric Learning -- Error Resilient Neural Networks on Low-Dimensional Manifolds -- On 4-Dimensional Hypercomplex Algebras in Adaptive Signal Processing -- Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines -- Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors -- Appraisal of Enhanced Surrogate Models for Substrate Integrate Waveguide Devices Characterization -- An Improved PSO for Flexible Parameters Identification of Lithium Cells Equivalent Circuit Models -- New Challenges in Pension Industry: Proposals of Personal Pension Products -- A Method Based on OWA Operator for Scientific Research Evaluation -- A Cluster Analysis Approach for Rule Base Reduction.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-95098-3
ISBN:	9783319950983

Neural advances in processing nonlinear dynamic signals
Neural advances in processing nonlinear dynamic signals[electronic resource] /edited by Anna Esposito ... [et al.]. - Cham :Springer International Publishing :2019. - xii, 318 p. :ill., digital ;24 cm. - Smart innovation, systems and technologies,v.1022190-3018 ;. - Smart innovation, systems and technologies ;v.102..

Processing Nonlinearities -- Temporal Artifacts from Edge Accumulation in Social Interaction -- Data Mining by Evolving Agents for Clusters Discovery and Metric Learning -- Error Resilient Neural Networks on Low-Dimensional Manifolds -- On 4-Dimensional Hypercomplex Algebras in Adaptive Signal Processing -- Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines -- Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors -- Appraisal of Enhanced Surrogate Models for Substrate Integrate Waveguide Devices Characterization -- An Improved PSO for Flexible Parameters Identification of Lithium Cells Equivalent Circuit Models -- New Challenges in Pension Industry: Proposals of Personal Pension Products -- A Method Based on OWA Operator for Scientific Research Evaluation -- A Cluster Analysis Approach for Rule Base Reduction.

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.

ISBN: 9783319950983

Standard No.: 10.1007/978-3-319-95098-3doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .N48 2019

Dewey Class. No.: 006.32

Neural advances in processing nonlinear dynamic signals
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520 $a 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.
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