Algebraic Training of a Neural Network

A novel algebraic neural network training technique is developed and demonstrated on two well-known architec-tures. This approach suggests an innovative, unified framew&k 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. 1