A novel algebraic neural network training technique is developed and demonstrated on two well-known architectures. This approach suggests an innovative, unified framework for analyzing neural approximation properties and for training neural networks in a much simplified way. Various implementations show that this approach presents numerous practical advantages; it provides a trouble-free non-iterative systematic procedure to integrate neural networks in control architectures, and it affords deep insight into neural nonlinear control system design.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the American Control Conference|
|State||Published - Jun 2001|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering