Neural network model with clustering ensemble approach

• US 20070150424A1
• Filed: 12/22/2005
• Published: 06/28/2007
• Est. Priority Date: 12/22/2005
• Status: Abandoned Application

• Current Assignee: Pegasus Technologies Incorporated (Evergreen Energy, Inc.)
• Original Assignee: Pegasus Technologies Incorporated (Evergreen Energy, Inc.)
• Inventors: Igelnik, Boris
• Application Number:
  US11/315,746
• Publication Number:
  US 20070150424A1
• Time in Patent Office: Days
• US Class Current: 706/15
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G06F 18/2136 based on sparsity criteria, e.g. with an overcomplete basis
• CPC Class Codes: G06F 18/23211 with adaptive number of clusters
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A predictive global model for modeling a system includes a plurality of local models, each having: an input layer for mapping into an input space, a hidden layer and an output layer. The hidden layer stores a representation of the system that is trained on a set of historical data, wherein each of the local models is trained on only a select and different portion of the set of historical data. The output layer is operable for mapping the hidden layer to an associated local output layer of outputs, wherein the hidden layer is operable to map the input layer through the stored representation to the local output layer. A global output layer is provided for mapping the outputs of all of the local output layers to at least one global output, the global output layer generalizing the outputs of the local models across the stored representations therein.

Claims

12. A predictive system for modeling the operation of at least one output of a process that operates in defined operating regions of an input space;

comprising;

a set of training data of input values and corresponding measured output values for the at least one output of the process taken during the operation of the process within the defined operating regions;

a plurality of local models of the process, each associated with one of the defined operating regions and each trained on the portion of said training data for the defined operating region associated therewith;

a generalization model for combining the outputs of all of said plurality of local models to provide a global output corresponding to the at least one output of the process, wherein said global model is trained on substantially all of said training data, with said local models remaining fixed during the training of said generalization model.

13. The system of claim 12, wherein each of said local models comprises:

an input layer for mapping into an input space of inputs associated with the inputs to the process,

a hidden layer for storing a representation of the process that is trained on the portion of said training data for the defined operating region associated therewith, and

an output layer for mapping to an associated at least one output,

wherein said hidden layer is operable to map said input layer through said stored representation to the at least one output.

14. The system of claim 13, wherein said data in said training data set is arranged in clusters, each with a center of mass in the input space with the remaining of the portion of said training data in the cluster being in close association therewith and each of said local models associated with one of said clusters.

15. The system of claim 14, wherein each of said local models comprises a non-linear model.

16. The system of claim 14, wherein said generalization model comprises a plurality of global weights and the at least one output of each of said local models are mapped to said at least one global output through an associated one of said global weights by the following relationship:

$N<br><br>\left(x\right)=c_0+\underset{j=1}{\overset{c}{\sum <br><br>}}<br><br>c_c<br><br>j<br><br>\text{\hspace{1em}<br><br>}<br><br>N_j<br><br>\left(x\right),$

where the set of global weights is (c₀, c₁, . . . , c_c) and N_j comprises the at least one output of said associated local model.

17. The system of claim 16, wherein said global weights are trained on substantially all of the training data with the representation stored in each of said local models remaining fixed.

18. The system of claim 17, wherein said output layer of each of said local models is trained with a recursive linear regression (RLR) algorithm.

19. The system of claim 17, and further comprising a storage device for storing the output values from said local models during training thereof in conjunction with said historical data set for each of said local models.

20. The system of claim 17, and further comprising an adaptive system for retraining the global model when new measured data is present.

21. The system of claim 20, wherein said adaptive system comprises:

a data set modifier for including the new data in said training data;

a cluster detector to determine the closest one of said clusters to the new data and modifying said determined one of said closest one of said clusters to include the new data;

a local model retraining system for retraining only the one of said local models associated with said modified cluster; and

a global output layer retraining system for retraining said global output layer.

22. The system of claim 21, and further comprising a storage device for storing the output values from said local models during training in conjunction with said training data for each of said local models.

23. The system of claim 22, wherein said local model retraining system is operable to update the contents of said storage device after retraining of said local model and said global output layer retraining system utilizes only the contents of said storage system during retraining, such that reprocessing of training data through said local models is not required.

24. A controller for controlling a process, comprising:

a control input to the process and measurable outputs from the process; and

a control system operable to receive the measurable outputs from the process and generate control inputs thereto, said control system including a predictive model having;

a plurality of local models of the process, each associated with one of a plurality of defined operating regions of the process and each trained on training data associated with the associated defined operating region, and

a generalization model for combining the outputs of all of said plurality of local models to provide a global output corresponding to at least one output of the process, wherein said global model is trained on substantially all of said training data on which each of said local models was trained, with said local models remaining fixed during the training of said generalization model, and

said predictive model utilized in generating the control inputs to the process.

1. A predictive global model for modeling a system, comprising:

a plurality of local models, each having;

an input layer for mapping into an input space,

a hidden layer for storing a representation of the system that is trained on a set of historical data, wherein each of said local models is trained on only a select and different portion of the historical data, and

an output layer for mapping to an associated at least one local output,

wherein said hidden layer is operable to map said input layer through said stored representation to said at least one local output; and

a global output layer for mapping the at least one outputs of all of said local models to at least one global output, said global output layer generalizing said at least one outputs of said local models across the stored representations therein.

2. The system of claim 1, wherein said data in said historical data set is arranged in clusters, each with a center in the input data space with the remaining data in the cluster being in close association therewith and each of said local models associated with one of said clusters.

3. The system of claim 2, wherein each of said local models comprises a non-linear model.

4. The system of claim 2, wherein said global output layer comprises a plurality of global weights and said at least one output of said local models are mapped to said at least one global output through an associated one of said global weights by the following relationship:

$N<br><br>\left(x\right)=c_0+\underset{j=1}{\overset{c}{\sum <br><br>}}<br><br>c_j<br><br>{\tilde{N}}_j<br><br>\left(x\right),$

where the set of global weights is (c₀, c₁, . . . , c_c) and N_j comprises the at least one output of said associated local model.

5. The system of claim 4, wherein said global weights are trained on the data set comprised of the input data in said historical data set and associated outputs of said local models, such that said global output layer comprises a linear model.

6. The system of claim 5, wherein said output layer is trained with a recursive linear regression (RLR) algorithm.

7. The system of claim 5, and further comprising a storage device for storing the output values from said local models during training in conjunction with said historical data set for each of said local models.

8. The system of claim 5, and further comprising an adaptive system for retraining the global model when new data is present.

9. The system of claim 8, wherein said adaptive system comprises:

a data set modifier for including the new data in said historical data set;

a cluster detector to determine the closest one of said clusters to the new data and modifying said determined one of said closest one of said clusters to include the new data;

a local model retraining system for retraining only the one of said local models associated with said modified cluster; and

a global output layer retraining system for retraining said global output layer.

10. The system of claim 9, and further comprising a storage device for storing the output values from said local models during training in conjunction with said historical data set for each of said local models.

11. The system of claim 10, wherein said local model retraining system is operable to update the contents of said storage device after retaining of said local model and said global output layer retraining system utilizes only the contents of said storage system during retraining, such that reprocessing of training data through said local models is not required.

25. The controller of claim 24, wherein said control system is operable to control air emissions from the process from the group consisting of NOx, CO, mercury and CO₂.

26. The controller of claim 24, wherein the process is a power generation plant and said control system is operable to control operating parameters of the plant consisting of the one or more elements of the group consisting of NOx, CO, steam reheat, temperature, boiler efficiency opacity and heat rate.

27. The controller of claim 26, wherein said load range is comprised of the group consisting of a low load range, a mid load range and a high load range.

28. The system of claim 24, wherein each of said local models comprises:

an input layer for mapping into an input space of inputs associated with the inputs to the process,

a hidden layer for storing a representation of the process that is trained on said training data associated with the defined operating region; and

an output layer for mapping to an associated at least one output,

wherein said hidden layer is operable to map said input layer through said stored representation to the at least one output.

29. The system of claim 28, wherein said data in each said training data associated with each of said defined regions is arranged in clusters, each with a center of mass in the input space with the remaining of the portion of said training data in the cluster being in close association therewith and each of said local models associated with one of said clusters.

30. The system of claim 29, wherein each of said local models comprises a non-linear model.

31. The system of claim 29, wherein said generalization model comprises a plurality of global weights and the at least one output of each of said local models are mapped to said at least one global output through an associated one of said global weights by the following relationship:

$N<br><br>\left(x\right)=c_0+\underset{j=1}{\overset{c}{\sum <br><br>}}<br><br>c_j<br><br>{\tilde{N}}_j<br><br>\left(x\right),$

where the set of global weights is (c₀, c₁, . . . , c_c) and N_j comprises the at least one output of said associated local model.

32. The system of claim 24, wherein said global weights are trained on substantially all of the training data associated with all of said defined regions with the representation stored in each of said local models remaining fixed.

33. The system of claim 32, wherein said output layer of each of said local models is trained with a recursive linear regression (RLR) algorithm.

34. The system of claim 32, and further comprising a storage device for storing the output values from said local models during training thereof in conjunction with said historical data set for each of said local models.

35. The system of claim 32, and further comprising an adaptive system for retaining the global model when new measured data is present.

36. The system of claim 35, wherein said adaptive system comprises:

a data set modifier for including the new data in said training data for select ones of said defined regions;

a cluster detector to determine the closest one of said clusters to the new data and modifying said determined one of said closest one of said clusters to include the new data;

a local model retraining system for retraining only the one of said local models associated with said modified cluster; and

a global output layer retraining system for retraining said global output layer.

37. The system of claim 36, and further comprising a storage device for storing the output values from said local models during training in conjunction with said training data for each of said local models.

38. The system of claim 37, wherein said local model retraining system is operable to update the contents of said storage device after retraining of said local model and said global output layer retraining system utilizes only the contents of said storage system during retraining, such that reprocessing of training data through said local models is not required.

39. The system of claim 24, wherein control system utilizes an optimizer in conjunction with the model to determine manipulated variables that comprise inputs to the process.

26-1. The controller of claim 24, wherein the process is a power generation plant and each of said local nets and its associated defined region comprises a load range of the power generation plant.