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 c...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2019.
|
| Έκδοση: | 1st ed. 2019. |
| Σειρά: | Smart Innovation, Systems and Technologies,
102 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
