Neural Advances in Processing Nonlinear Dynamic Signals
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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- Anna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, Eros Pasero
- Hardback | 318 pages
- 155 x 235 x 19.05mm | 664g
- Publication date
- 22 Aug 2018
- Springer International Publishing AG
- Publication City/Country
- Cham, Switzerland
- Edition Statement
- 1st ed. 2019
- Illustrations note
- 61 Illustrations, color; 30 Illustrations, black and white; XII, 318 p. 91 illus., 61 illus. in color.