東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Neural advances in processing nonlin...

Esposito, Anna.

Linked to FindBook

Google Book

Amazon

博客來

Neural advances in processing nonlinear dynamic signals

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural advances in processing nonlinear dynamic signals/ edited by Anna Esposito ... [et al.].
other author:	Esposito, Anna.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xii, 318 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Neural networks (Computer science) -
Online resource:	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
LDR:03139nmm a2200325 a 4500 001 2176552
003 DE-He213
005 20180721122314.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783319950983 $q (electronic bk.)
020 $a 9783319950976 $q (paper)
024 7 $a 10.1007/978-3-319-95098-3 $2 doi
035 $a 978-3-319-95098-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .N48 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .N494 2019
245 0 0 $a Neural advances in processing nonlinear dynamic signals $h [electronic resource] / $c edited by Anna Esposito ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xii, 318 p. : $b ill., digital ; $c 24 cm.
490 1 $a Smart innovation, systems and technologies, $x 2190-3018 ; $v v.102
505 0 $a 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.
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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Computer algorithms. $3 523872
650 0 $a Nonlinear systems. $3 686475
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Complexity. $3 893807
700 1 $a Esposito, Anna. $3 919287
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Smart innovation, systems and technologies ; $v v.102. $3 3378755
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-95098-3
950 $a Engineering (Springer-11647)