Abstract
Neural networks are now being extensively used as feedback controllers. We overview the basic approaches to neurocontroller development and concentrate our attention on the 'model-based neurocontrol design' approach. Controller design is viewed as an optimization problem, and a basic distinction is made between gradient-based and non-gradient-based algorithms. The former impose constraints on the design problem in order to facilitate the computational aspects of the optimization, whereas nongradient-based optimization allows for general problem formulations but at significant computational cost. The 'Parametrized Neurocontroller' concept is discussed to motivate the need for nongradient-based optimization and an evolutionary optimization algorithm is presented.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2486-2490 |
| Number of pages | 5 |
| Journal | Proceedings of the American Control Conference |
| Volume | 3 |
| State | Published - Dec 1 1994 |
| Event | Proceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA Duration: Jun 29 1994 → Jul 1 1994 |