Neural regulator design.
basic_science · Level V
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Abstract
Design of a neural-net-based regulator for nonlinear plants is considered. Both state and output feedback regulators with deterministic and stochastic disturbances have been investigated. A Multilayered Feedforward Neural Network (MFNN) has been employed as the nonlinear controller. The training of the MFNN utilizes the recently developed concept of Block Partial Derivatives (BPDs).