INDICE: 1. Basic classification of Non Linear (NL) representations of signals: with or without memory 1.1 Memoryless systems effects on signals: Probability Density transformations. Random Processes Moment transformations: Price theorem and its generalizations. 1.2 Time Dependent NL signal models: integral and differential equations (Fredholm, Volterra, etc.). 2. Modeling Non–Linear systems 2.1 Hammerstein separable Models 2.2 Cellular networks: Neural Networks, Support Vector Machines 2.3 State Space Equation based: Extended Kalman Filter 3. Parameter estimation in NL systems 3.1 Known Input Methods: Kalman, Least Squares and Recursive Least Squares, Supervised (i.e. ?with learning phase?): Neural Networks 3.2 Self–learning mode: Kohonen–like algorithms 4. Selected application examples derived from: 4.1 Basic Signal Processing: Polynomial NL systems, hard–limiters, clippers, etc. 4.2 Space Telecommunications: Satellite On–board Solid State Power Amplifier, Non–Linear Channel Equalizers.
- ISBN: 978-1-84821-456-9
- Editorial: ISTE Ltd.
- Encuadernacion: Cartoné
- Páginas: 160
- Fecha Publicación: 03/09/2014
- Nº Volúmenes: 1
- Idioma: Inglés