Total least squares in fuzzy system identification: An application to an industrial engine

Jakubek, Stefan; Hametner, Christoph; Keuth, Nikolaus

doi:10.1016/j.engappai.2008.04.020

Cited by 33 publications

(10 citation statements)

References 21 publications

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“…2 the identification data are shown and local models are represented by contour lines of their validity functions. Those models that are intersected by the equilibrium line are equilibrium models whereas models number five and six are off-equilibrium models with unstable local dynamics (Jakubek et al, 2008). As it is the minimum-phase property only, which has to be guaranteed, feedforward control can be applied.…”

Section: Resultsmentioning

confidence: 99%

Automatic Generation of Feedforward Controllers using Dynamic Local Model Networks

Hametner¹,

Jakubek²,

Euler-Rolle

2014

IFAC Proceedings Volumes

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Section: Resultsmentioning

confidence: 99%

Automatic Generation of Feedforward Controllers using Dynamic Local Model Networks

Hametner¹,

Jakubek²,

Euler-Rolle

2014

IFAC Proceedings Volumes

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“…Several applications of TLS exist in the real world, such as fuzzy system identification of an industrial gas engine power plant [1], blind deconvolution problems as encountered in image deblurring when both the image and the blurring function have uncertainty [2], and applications to astronomy and geodesy [3]. An overview of TLS can be found in [4].…”

Section: Introductionmentioning

confidence: 99%

Error-Covariance Analysis of the Total Least-Squares Problem

Cheng¹

2014

Journal of Guidance, Control, and Dynamics

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This paper derives and analyzes the estimate error covariances associated with both the nonstationary and stationary noise process cases with uncorrelated elementwise components for the total least-squares problem. The nonstationary case is derived directly from the associated unconstrained total least-squares loss function. The stationary case is derived by using a linear expansion of the total least-squares estimate equation, which involves a first-order expansion of the associated singular value decomposition matrices. The actual solution for the error covariance is evaluated at the true variables, which are unknown in practice. Two common approaches to overcome this difficulty are used; the first involves using the measurements directly and the second involves using the estimates, which are more accurate than the measurements. This paper shows that using the latter greatly simplifies the errorcovariance solution for the stationary case. Simulation results using bearings-only point estimation are shown to quantify the theoretical derivations.

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“…A fuzzy control strategy that depends on the Takagi-Sugeno modeling and robust control approach was applied to a three-cylinder SI engine (Khiar et al, 2007). The fuzzy inference system has been found to be a domain for numerous successful applications, such as the neuro-fuzzy identification of a pilot refrigeration plant (Franco et al, 2011), nonlinear modeling of a solid oxide fuel cell based on ANFIS identification (Wu, Zhu, Cao, & Tu, 2008), identification of a bioreactor using a neural network and ANFIS (Efe, 2010;Savran & Kahraman, 2014), identification of a nuclear power plant transient based on a fuzzy approach (Da Costa, Mol, De Carvalho, & Lapa, 2011;Marseguerra, Zio, Oldrini, & Brega, 2003), two-way fuzzy adaptive identification and control of a flexible-joint robot arm (Gurkan, Erkmen, & Erkmen, 2002), fuzzy identification of an industrial engine (Jakubek, Hametner, & Keuth, 2008), ANFIS identification of a water level system (Turki, Bouzaida, & Sakly, 2013), neuro-fuzzy identification and control of simulation examples (Baruch, Lopez, Guzman, & Flores, 2008), and identification of waste packaging profiles using fuzzy logic (Olveraa, Benítez, Rodríguez, Zanoguera, & Diaz, 2008). Moreover, fuzzy control has been applied to an expert heating ventilating and air-conditioning system (Soyguder & Alli, 2009), an air system in a test rig of low-temperature hot water radiator system (Lu, Zhang, Chen, Zhao, & Liu, 2010), and an electrical drive system (Eker & Torun, 2006).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

Toğun

Bayseç

2016

Expert Systems

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Spark ignition (SI) engines have a nonlinear dynamic system with inherent uncertainties and unpredictable disturbances. The identification of a nonlinear system is vital in many fields of engineering. In this study, SI engine torque is identified from an input–output measurement. This study aims to propose a dynamic nonlinear model that uses an adaptive neuro‐fuzzy inference system and a nonlinear auto‐regressive with exogenous input structure to identify the dynamic nonlinear behavior of an SI engine. Considerable good performance is achieved using the adaptive neuro‐fuzzy inference system nonlinear auto‐regressive with exogenous input method. For model validation, the proposed method is compared with the more conventional identification approach called the Hammerstein method. The results show that the two methods are in excellent agreement. The Hammerstein model was chosen because its identification result of the SI system was studied previously by the author. Validation results prove that the ability of the proposed model can capture the highly nonlinear behavior of the SI system.

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Total least squares in fuzzy system identification: An application to an industrial engine

Cited by 33 publications

References 21 publications

Automatic Generation of Feedforward Controllers using Dynamic Local Model Networks

Automatic Generation of Feedforward Controllers using Dynamic Local Model Networks

Error-Covariance Analysis of the Total Least-Squares Problem

Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

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