Name
Title | ||
|---|---|---|
Adaptive identification and predictive control using an improved on-line sequential extreme learning machine |
Abstract | ||
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This paper proposes a method for adaptive identification and predictive control using an online sequential extreme learning machine based on the recursive partial least-squares method (OS-ELM-RPLS). OL-ELM-RPLS is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OS-ELM-RLS the input weights of a single-hidden layer feedforward neural network (SLFN) are randomly generated, however the output weights are obtained by a recursive least-squares (RLS) solution. However, due to multicollinearities in the columns of the hidden-layer output matrix caused by the presence of redundant input variables or by a large number of hidden-layer neurons, the problem of estimation the output weights can become ill-conditioned. In order to circumvent or mitigate such ill-conditioning problem, it is proposed to replace the RLS method by the recursive partial least-squares (RPLS) method. The identification methodology is proposed for two application problems: (1) construction of a inferential model, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an adaptive predictive control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on modeling of two public regression data sets and on control of the flow through a simulated valve. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IECON.2014.7048477 | IECON |
Keywords | Field | DocType |
adaptive control,feedforward neural nets,identification,learning (artificial intelligence),learning systems,least squares approximations,neurocontrollers,predictive control,regression analysis,gpc algorithm,os-elm-rpls,slfn,adaptive identification,adaptive predictive control methodology,batch extreme learning machine,generalized predictive control,hidden-layer neurons,hidden-layer output matrix,identification methodology,ill-conditioning problem,inferential model,multicollinearities,online sequential extreme learning machine,output weights,recursive partial least-squares method,redundant input variables,regression data sets,single-hidden layer feedforward neural network,predictive models,vectors,tin | Data set,Feedforward neural network,Adaptive identification,Regression,Extreme learning machine,Control theory,Computer science,Matrix (mathematics),Model predictive control,Control engineering,Recursion | Conference |
ISSN | Citations | PageRank |
1553-572X | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiago Matias | 1 | 35 | 4.65 |
| Francisco Souza | 2 | 56 | 7.08 |
| Rui Araújo | 3 | 164 | 18.93 |
| saeid rastegar | 4 | 4 | 1.90 |
| Jérôme Mendes | 5 | 44 | 6.53 |