Neural networks provide several distinctive features that are of substantial relevance to control technology. These include: accurate approximations of nonlinear functions and nonlinear dynamical systems; compact, efficient implementations; and data-intensive rather than expertise-intensive model and controller development. The benefits of neural networks for control applications are now being realized in numerous domains. We discuss several ways neural networks can be used for modeling and control. For modeling applications, neural networks have been trained to realize "black-box" forward and inverse process models as well as parametric models. Neural network controllers can be developed by emulating existing controllers, by model-free optimization, and by model-based optimization. Examples from deployed applications, available products, and the technical literature illustrate these concepts. We conclude by discussing some important topics for future research: dynamic neural networks, incremental learning, and application-specific network design.
|Original language||English (US)|
|Number of pages||9|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - Sep 1 1993|
|Event||Adaptive and Learning Systems II 1993 - Orlando, United States|
Duration: Apr 11 1993 → Apr 16 1993