This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.
This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions.
1. Basic Principles: The Operating Regime Approach
2. Modelling: Fuzzy Set Methods for Local Modelling Identification
3. Modelling of Electrically Stimulated Muscle
4. Process Modelling Using a Functional State Approach
5. Markov Mixtures of Experts
6. Active Learning With Mixture Models
7. Local Learning in Local Model Networks
8. Side Effects of Normalising Basic Functions
9. Control: Heterogeneous Control Laws
10. Local Laguerre Models
11. Multiple Model Adaptive Control
12. H Control Using Multiple Linear Models
13. Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno Fuzzy Models
R Murray-Smith (Edited by) , T. Johansen (Edited by)
