Neural Advances in Processing Nonlinear Dynamic Signals (Paperback, Softcover reprint of the original 1st ed. 2019)

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

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Smart Innovation, Systems and Technologies, 102
Release date:	February 2019
First published:	2019
Editors:	Anna Esposito • Marcos Faundez-Zanuy • Francesco Carlo Morabito • Eros Pasero
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Paperback
Pages:	318
Edition:	Softcover reprint of the original 1st ed. 2019
ISBN-13:	978-3-03-006977-3
Categories:	Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-006977-X
Barcode:	9783030069773

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