Dynamical systems with saturation nonlinearities : analysis and design
This three-part monograph addresses topics in the areas of control systems, signal processing and neural networks. Procedures and results are determined which constitute the first successful synthesis procedure for associative memories by means of artificial neural networks with arbitrarily pre-spec...
Tallennettuna:
| Päätekijät: | , |
|---|---|
| Aineistotyyppi: | Livre numérique |
| Kieli: | Anglais |
| Julkaistu: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Sarja: | Lecture notes in control and information sciences
195 |
| Aiheet: | |
| Linkit: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Huomautus: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Dynamical systems with saturation nonlinearities, analysis and design, Derong Liu and Anthony N. Michel, London, Springer-Verlag, 1994, 1 vol. (xiv-191 p.), Lecture notes in control and information sciences, 3-540-19888-1 • Dynamical Systems with Saturation Nonlinearities, Texte imprimé, 9783662161906 |
Sisällysluettelo:
- to dynamical systems with saturation nonlinearities
- Qualitative theory of control systems with control constraints and state saturation: Two fundamental issues
- Asymptotic stability of dynamical systems with state saturation
- Null controllability of discrete-time dynamical systems with control constraints and state saturation
- Stability analysis of one-dimensional and multidimesional state-space digital filters with overflow nonlinearities
- Criteria for the absence of overflow oscillations in fixed-point digital filters using generalized overflow characteristics
- Stability analysis of state-space realizations for multidimensional filters with overflow nonlinearities
- to part III
- Analysis and synthesis of a class of neural networks with piecewise linear saturation activation functions
- Sparsely interconnected neural networks for associative memories with applications to cellular neural networks
- Robustness analysis of a class sparsely interconnected neural networks with applications to design problem.