Please use this identifier to cite or link to this item:
https://repository.unej.ac.id/xmlui/handle/123456789/97247
Title: | Hierarchical Linear and Nonlinear Adaptive Learning Model for System Identification and Prediction |
Authors: | Jami’in, Mohammad Abu Anam, Khairul Rulaningtyas, Riries Mudjiono, Urip Adianto, Adianto Hui-Ming Wee |
Keywords: | System identification Hierarchical algorithm Adaptive learning Prediction Parameter estimation |
Issue Date: | 10-Feb-2020 |
Publisher: | SPRINGER - Applied Intelligence, Online First Articles, Februari 2020 |
Abstract: | In this paper, we propose a method to increase the model accuracy with linear and nonlinear sub-models. The linear submodel applies the least square error (LSE) algorithm and the nonlinear sub-model uses neural networks (NN). The two sub-models are updated hierarchically using the Lyapunov function. The proposed method has two advantages: 1) The neural networks is a multi-parametric model. Using the proposed model, the weights of NN model can be summarized into the coefficients or parameters of auto-regressive eXogenous/auto-regressive moving average (ARX/ARMA) model structure, making it easier to establish control laws, 2) learning rate is updated to ensure the convergence of errors at each training epoch. One can improve the accuracy of model and the whole control system. We have demonstrated by the experimental studies that the proposed technique gives better results when compared to the existing studies. |
URI: | http://repository.unej.ac.id/handle/123456789/97247 |
Appears in Collections: | LSP-Jurnal Ilmiah Dosen |
Files in This Item:
File | Description | Size | Format | |
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F. T_Jurnal_Khairul Anam_Hierarchical linear and nonlinear adaptive.pdf | 2.1 MB | Adobe PDF | View/Open |
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