Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

Farah Hani NORDIN; Farrukh Hafiz NAGI; Aidil Azwin Zainul ABIDIN

Makale detayları

Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

Yazarlar:

Farah Hani NORDIN | Farrukh Hafiz NAGI | Aidil Azwin Zainul ABIDIN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1151-1165

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743277

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

To determine the nonlinear autoregressive model with  exogenous inputs (NARX) parameter values is not an easy task, even  though NARX is reported to successfully identify nonlinear systems.  Apart from the activation functions, number of layers, layer size,  learning rate, and number of epochs, the number of delays at the input  and at the feedback loop need to also be determined.  The layer  recurrent network (LRN) is seen to have the potential to outperform  NARX. However, not many papers have reported on using the LRN to  identify nonlinear systems.  Therefore, it is the aim of this paper to  investigate and analyze the parametric evaluation of the LRN and NARX  in identifying 3 different types of nonlinear systems.  From the 3  nonlinear systems, the satellite's attitude state space is more  complex compared to the sigmoid and polynomial equations.  To ensure  an unbiased comparison, a general guideline is used to select the  parameter values in an organized manner.  The LRN and NARX performance  is analyzed based on the training and architecture parameters, mean  squared errors, and correlation coefficient values.  The results show  that the LRN outperformed NARX in training quality, needs equal or  fewer parameters that need to be determined through heuristic  processes and equal or lower number of epochs, and produced a smaller  training error compared to NARX, especially when identifying the  satellite's attitude.  This indicates that the LRN has the capability  of identifying a more complex and nonlinear system compared to NARX.

 

To determine the nonlinear autoregressive model with  exogenous inputs (NARX) parameter values is not an easy task, even  though NARX is reported to successfully identify nonlinear systems.  Apart from the activation functions, number of layers, layer size,  learning rate, and number of epochs, the number of delays at the input  and at the feedback loop need to also be determined.  The layer  recurrent network (LRN) is seen to have the potential to outperform  NARX. However, not many papers have reported on using the LRN to  identify nonlinear systems.  Therefore, it is the aim of this paper to  investigate and analyze the parametric evaluation of the LRN and NARX  in identifying 3 different types of nonlinear systems.  From the 3  nonlinear systems, the satellite's attitude state space is more  complex compared to the sigmoid and polynomial equations.  To ensure  an unbiased comparison, a general guideline is used to select the  parameter values in an organized manner.  The LRN and NARX performance  is analyzed based on the training and architecture parameters, mean  squared errors, and correlation coefficient values.  The results show  that the LRN outperformed NARX in training quality, needs equal or  fewer parameters that need to be determined through heuristic  processes and equal or lower number of epochs, and produced a smaller  training error compared to NARX, especially when identifying the  satellite's attitude.  This indicates that the LRN has the capability  of identifying a more complex and nonlinear system compared to NARX.

Makale detayları

Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

Yazarlar: Farah Hani NORDIN | Farrukh Hafiz NAGI | Aidil Azwin Zainul ABIDIN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1151-1165

Belge Tipi: Makale

Yayın Tarihi: 3/2013

Belge Dili: Türkçe

Document ID (Org No.): 2000743277

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Farah Hani NORDIN | Farrukh Hafiz NAGI | Aidil Azwin Zainul ABIDIN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1151-1165

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743277

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632