Neural Network Modeling and an Extended DMC Algorithm to Control Nonlinear Systems

By E. Hernandaz, Y. Arkun. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Michael Janner
3. Pieter Abbeel
4. Rowan Thomas McAllister
5. Kurtland Chua
6. Roberto Calandra
7. Danijar Hafner
8. Chelsea Finn

Citations in the corpus

• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “Parametric function approximators have also been used extensively in MBRL [21], [22], [23], [24], but”
• A hybrid neural network‐first principles approach to process modeling (1992, unknown): “Traditional neural network process models can be adapted as new data become available, but the generality of such an adapted model is questionable (Hernandez and Arkun, 1990) because all of the model's internal parameters are updated since all are considered partially responsible for the error.”