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1. WO2020159947 - COMMANDE SANS MODÈLE DE SYSTÈMES DYNAMIQUES AVEC CALCUL EN RÉSERVOIR PROFOND

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

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What is claimed:

1. A system comprising:

a first reservoir computer configured to control a plant displaying nonlinear dynamics; and

a second reservoir computer configured to control the first reservoir computer and the plant.

2. The system of claim 1, wherein each of the first reservoir computer and the second reservoir comprises a recurrent neural network.

3. The system of claim 1, further comprising a deep reservoir computer that comprises the first reservoir computer and the second reservoir computer, wherein the deep reservoir computer is configured to provide precise, model-free control of the plant.

4. The system of claim 1, wherein the first reservoir computer and the plant comprise a first layer, and the second reservoir computer is configured to train the first layer.

5. The system of claim 4, wherein the second reservoir computer and the first layer comprise a second layer, and further comprising a third reservoir computer configured to train the second layer.

6. The system of claim 5, wherein the first layer and the second layer form a deep recurrent neural network.

7. The system of claim 1, wherein the first reservoir computer and the second reservoir computer are comprised within an n-layer echo-state network (ESN) controller, or wherein each of the reservoir computers in the n-layer controller comprises a physical system.

8. The system of claim 1, wherein each of the first reservoir computer and the second reservoir computer comprises a physical system, such as an autonomous logic circuit or an optoelectronic system.

9. A method comprising:

configuring a first reservoir computer to control a plant; and

configuring a second reservoir computer to control the first reservoir computer and the plant.

10. The method of claim 9, wherein each of the first reservoir computer and the second reservoir comprises a recurrent neural network.

11. The method of claim 9, further comprising configuring a deep reservoir computer to provide precise, model-free control of the plant, wherein the deep reservoir computer comprises the first reservoir computer and the second reservoir computer.

12. The method of claim 9, wherein the first reservoir computer and the plant comprise a first layer, further comprising configuring the second reservoir computer to train the first layer.

13. The method of claim 12, wherein the second reservoir computer and the first layer comprise a second layer, further comprising configuring a third reservoir computer to train the second layer.

14. The method of claim 13, wherein the first layer and the second layer form a deep recurrent neural network.

15. A method comprising:

controlling a plant using a controller; and

controlling the controller and the plant using a reservoir computer.

16. The method of claim 15, wherein the controller comprises a linear proportional-integral-derivative controller (PID controller).

17. The method of claim 15, wherein the controller is a custom, model -based non-linear controller.

18. The method of claim 15, wherein the controller and the plant comprise a first layer, further comprising training the first layer using the reservoir computer.

19. The method of claim 18, wherein the reservoir computer and the first layer comprise a second layer, further comprising training the second layer using an additional reservoir computer.

20. The method of claim 19, further comprising forming a deep recurrent neural network using the first layer and the second layer.