Neural Advances in Processing Nonlinear Dynamic Signals [Hardcover]

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

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 CharactelóT